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  Subjects -> COMPUTER SCIENCE (Total: 2102 journals)
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COMPUTER SCIENCE (1221 journals)                  1 2 3 4 5 6 7 | Last

Showing 1 - 200 of 872 Journals sorted alphabetically
3D Printing and Additive Manufacturing     Full-text available via subscription   (Followers: 21)
Abakós     Open Access   (Followers: 4)
ACM Computing Surveys     Hybrid Journal   (Followers: 29)
ACM Journal on Computing and Cultural Heritage     Hybrid Journal   (Followers: 8)
ACM Journal on Emerging Technologies in Computing Systems     Hybrid Journal   (Followers: 16)
ACM Transactions on Accessible Computing (TACCESS)     Hybrid Journal   (Followers: 3)
ACM Transactions on Algorithms (TALG)     Hybrid Journal   (Followers: 15)
ACM Transactions on Applied Perception (TAP)     Hybrid Journal   (Followers: 5)
ACM Transactions on Architecture and Code Optimization (TACO)     Hybrid Journal   (Followers: 9)
ACM Transactions on Autonomous and Adaptive Systems (TAAS)     Hybrid Journal   (Followers: 9)
ACM Transactions on Computation Theory (TOCT)     Hybrid Journal   (Followers: 12)
ACM Transactions on Computational Logic (TOCL)     Hybrid Journal   (Followers: 3)
ACM Transactions on Computer Systems (TOCS)     Hybrid Journal   (Followers: 18)
ACM Transactions on Computer-Human Interaction     Hybrid Journal   (Followers: 15)
ACM Transactions on Computing Education (TOCE)     Hybrid Journal   (Followers: 6)
ACM Transactions on Design Automation of Electronic Systems (TODAES)     Hybrid Journal   (Followers: 6)
ACM Transactions on Economics and Computation     Hybrid Journal   (Followers: 1)
ACM Transactions on Embedded Computing Systems (TECS)     Hybrid Journal   (Followers: 3)
ACM Transactions on Information Systems (TOIS)     Hybrid Journal   (Followers: 20)
ACM Transactions on Intelligent Systems and Technology (TIST)     Hybrid Journal   (Followers: 8)
ACM Transactions on Interactive Intelligent Systems (TiiS)     Hybrid Journal   (Followers: 3)
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)     Hybrid Journal   (Followers: 9)
ACM Transactions on Reconfigurable Technology and Systems (TRETS)     Hybrid Journal   (Followers: 6)
ACM Transactions on Sensor Networks (TOSN)     Hybrid Journal   (Followers: 8)
ACM Transactions on Speech and Language Processing (TSLP)     Hybrid Journal   (Followers: 9)
ACM Transactions on Storage     Hybrid Journal  
ACS Applied Materials & Interfaces     Hybrid Journal   (Followers: 32)
Acta Automatica Sinica     Full-text available via subscription   (Followers: 2)
Acta Informatica Malaysia     Open Access  
Acta Universitatis Cibiniensis. Technical Series     Open Access  
Ad Hoc Networks     Hybrid Journal   (Followers: 11)
Adaptive Behavior     Hybrid Journal   (Followers: 10)
Advanced Engineering Materials     Hybrid Journal   (Followers: 28)
Advanced Science Letters     Full-text available via subscription   (Followers: 11)
Advances in Adaptive Data Analysis     Hybrid Journal   (Followers: 7)
Advances in Artificial Intelligence     Open Access   (Followers: 15)
Advances in Calculus of Variations     Hybrid Journal   (Followers: 4)
Advances in Catalysis     Full-text available via subscription   (Followers: 5)
Advances in Computational Mathematics     Hybrid Journal   (Followers: 19)
Advances in Computer Engineering     Open Access   (Followers: 4)
Advances in Computer Science : an International Journal     Open Access   (Followers: 14)
Advances in Computing     Open Access   (Followers: 2)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 55)
Advances in Engineering Software     Hybrid Journal   (Followers: 28)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 14)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 22)
Advances in Human-Computer Interaction     Open Access   (Followers: 20)
Advances in Materials Science     Open Access   (Followers: 14)
Advances in Operations Research     Open Access   (Followers: 12)
Advances in Parallel Computing     Full-text available via subscription   (Followers: 7)
Advances in Porous Media     Full-text available via subscription   (Followers: 5)
Advances in Remote Sensing     Open Access   (Followers: 49)
Advances in Science and Research (ASR)     Open Access   (Followers: 6)
Advances in Technology Innovation     Open Access   (Followers: 6)
AEU - International Journal of Electronics and Communications     Hybrid Journal   (Followers: 8)
African Journal of Information and Communication     Open Access   (Followers: 9)
African Journal of Mathematics and Computer Science Research     Open Access   (Followers: 4)
AI EDAM     Hybrid Journal   (Followers: 1)
Air, Soil & Water Research     Open Access   (Followers: 12)
AIS Transactions on Human-Computer Interaction     Open Access   (Followers: 6)
Algebras and Representation Theory     Hybrid Journal   (Followers: 1)
Algorithms     Open Access   (Followers: 11)
American Journal of Computational and Applied Mathematics     Open Access   (Followers: 5)
American Journal of Computational Mathematics     Open Access   (Followers: 4)
American Journal of Information Systems     Open Access   (Followers: 6)
American Journal of Sensor Technology     Open Access   (Followers: 4)
Anais da Academia Brasileira de Ciências     Open Access   (Followers: 2)
Analog Integrated Circuits and Signal Processing     Hybrid Journal   (Followers: 7)
Analysis in Theory and Applications     Hybrid Journal   (Followers: 1)
Animation Practice, Process & Production     Hybrid Journal   (Followers: 5)
Annals of Combinatorics     Hybrid Journal   (Followers: 4)
Annals of Data Science     Hybrid Journal   (Followers: 12)
Annals of Mathematics and Artificial Intelligence     Hybrid Journal   (Followers: 13)
Annals of Pure and Applied Logic     Open Access   (Followers: 3)
Annals of Software Engineering     Hybrid Journal   (Followers: 13)
Annals of West University of Timisoara - Mathematics and Computer Science     Open Access  
Annual Reviews in Control     Hybrid Journal   (Followers: 8)
Anuario Americanista Europeo     Open Access  
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 2)
Applied and Computational Harmonic Analysis     Full-text available via subscription   (Followers: 1)
Applied Artificial Intelligence: An International Journal     Hybrid Journal   (Followers: 12)
Applied Categorical Structures     Hybrid Journal   (Followers: 2)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 13)
Applied Computer Systems     Open Access   (Followers: 2)
Applied Informatics     Open Access  
Applied Mathematics and Computation     Hybrid Journal   (Followers: 33)
Applied Medical Informatics     Open Access   (Followers: 10)
Applied Numerical Mathematics     Hybrid Journal   (Followers: 5)
Applied Soft Computing     Hybrid Journal   (Followers: 16)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 5)
Applied System Innovation     Open Access  
Architectural Theory Review     Hybrid Journal   (Followers: 3)
Archive of Applied Mechanics     Hybrid Journal   (Followers: 5)
Archive of Numerical Software     Open Access  
Archives and Museum Informatics     Hybrid Journal   (Followers: 145)
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 5)
arq: Architectural Research Quarterly     Hybrid Journal   (Followers: 8)
Artifact     Hybrid Journal   (Followers: 2)
Artificial Life     Hybrid Journal   (Followers: 7)
Asia Pacific Journal on Computational Engineering     Open Access  
Asia-Pacific Journal of Information Technology and Multimedia     Open Access   (Followers: 1)
Asian Journal of Computer Science and Information Technology     Open Access  
Asian Journal of Control     Hybrid Journal  
Assembly Automation     Hybrid Journal   (Followers: 2)
at - Automatisierungstechnik     Hybrid Journal   (Followers: 1)
Australian Educational Computing     Open Access   (Followers: 1)
Automatic Control and Computer Sciences     Hybrid Journal   (Followers: 5)
Automatic Documentation and Mathematical Linguistics     Hybrid Journal   (Followers: 5)
Automatica     Hybrid Journal   (Followers: 13)
Automation in Construction     Hybrid Journal   (Followers: 7)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 8)
Basin Research     Hybrid Journal   (Followers: 5)
Behaviour & Information Technology     Hybrid Journal   (Followers: 52)
Big Data and Cognitive Computing     Open Access   (Followers: 2)
Biodiversity Information Science and Standards     Open Access  
Bioinformatics     Hybrid Journal   (Followers: 308)
Biomedical Engineering     Hybrid Journal   (Followers: 16)
Biomedical Engineering and Computational Biology     Open Access   (Followers: 13)
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 20)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 35)
Briefings in Bioinformatics     Hybrid Journal   (Followers: 49)
British Journal of Educational Technology     Hybrid Journal   (Followers: 149)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 12)
c't Magazin fuer Computertechnik     Full-text available via subscription   (Followers: 1)
CALCOLO     Hybrid Journal  
Calphad     Hybrid Journal   (Followers: 2)
Canadian Journal of Electrical and Computer Engineering     Full-text available via subscription   (Followers: 15)
Capturing Intelligence     Full-text available via subscription  
Catalysis in Industry     Hybrid Journal   (Followers: 1)
CEAS Space Journal     Hybrid Journal   (Followers: 2)
Cell Communication and Signaling     Open Access   (Followers: 2)
Central European Journal of Computer Science     Hybrid Journal   (Followers: 5)
CERN IdeaSquare Journal of Experimental Innovation     Open Access   (Followers: 3)
Chaos, Solitons & Fractals     Hybrid Journal   (Followers: 3)
Chemometrics and Intelligent Laboratory Systems     Hybrid Journal   (Followers: 15)
ChemSusChem     Hybrid Journal   (Followers: 7)
China Communications     Full-text available via subscription   (Followers: 8)
Chinese Journal of Catalysis     Full-text available via subscription   (Followers: 2)
CIN Computers Informatics Nursing     Hybrid Journal   (Followers: 11)
Circuits and Systems     Open Access   (Followers: 15)
Clean Air Journal     Full-text available via subscription   (Followers: 1)
CLEI Electronic Journal     Open Access  
Clin-Alert     Hybrid Journal   (Followers: 1)
Clinical eHealth     Open Access  
Cluster Computing     Hybrid Journal   (Followers: 2)
Cognitive Computation     Hybrid Journal   (Followers: 4)
COMBINATORICA     Hybrid Journal  
Combinatorics, Probability and Computing     Hybrid Journal   (Followers: 4)
Combustion Theory and Modelling     Hybrid Journal   (Followers: 14)
Communication Methods and Measures     Hybrid Journal   (Followers: 12)
Communication Theory     Hybrid Journal   (Followers: 23)
Communications Engineer     Hybrid Journal   (Followers: 1)
Communications in Algebra     Hybrid Journal   (Followers: 3)
Communications in Computational Physics     Full-text available via subscription   (Followers: 2)
Communications in Information Science and Management Engineering     Open Access   (Followers: 4)
Communications in Partial Differential Equations     Hybrid Journal   (Followers: 3)
Communications of the ACM     Full-text available via subscription   (Followers: 51)
Communications of the Association for Information Systems     Open Access   (Followers: 16)
COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering     Hybrid Journal   (Followers: 3)
Complex & Intelligent Systems     Open Access   (Followers: 1)
Complex Adaptive Systems Modeling     Open Access  
Complex Analysis and Operator Theory     Hybrid Journal   (Followers: 2)
Complexity     Hybrid Journal   (Followers: 6)
Complexus     Full-text available via subscription  
Composite Materials Series     Full-text available via subscription   (Followers: 8)
Computación y Sistemas     Open Access  
Computation     Open Access   (Followers: 1)
Computational and Applied Mathematics     Hybrid Journal   (Followers: 3)
Computational and Mathematical Biophysics     Open Access   (Followers: 1)
Computational and Mathematical Methods in Medicine     Open Access   (Followers: 2)
Computational and Mathematical Organization Theory     Hybrid Journal   (Followers: 2)
Computational and Structural Biotechnology Journal     Open Access   (Followers: 2)
Computational and Theoretical Chemistry     Hybrid Journal   (Followers: 9)
Computational Astrophysics and Cosmology     Open Access   (Followers: 1)
Computational Biology and Chemistry     Hybrid Journal   (Followers: 12)
Computational Chemistry     Open Access   (Followers: 2)
Computational Cognitive Science     Open Access   (Followers: 2)
Computational Complexity     Hybrid Journal   (Followers: 4)
Computational Condensed Matter     Open Access  
Computational Ecology and Software     Open Access   (Followers: 9)
Computational Economics     Hybrid Journal   (Followers: 9)
Computational Geosciences     Hybrid Journal   (Followers: 17)
Computational Linguistics     Open Access   (Followers: 23)
Computational Management Science     Hybrid Journal  
Computational Mathematics and Modeling     Hybrid Journal   (Followers: 8)
Computational Mechanics     Hybrid Journal   (Followers: 5)
Computational Methods and Function Theory     Hybrid Journal  
Computational Molecular Bioscience     Open Access   (Followers: 2)
Computational Optimization and Applications     Hybrid Journal   (Followers: 8)
Computational Particle Mechanics     Hybrid Journal   (Followers: 1)
Computational Research     Open Access   (Followers: 1)
Computational Science and Discovery     Full-text available via subscription   (Followers: 2)
Computational Science and Techniques     Open Access  
Computational Statistics     Hybrid Journal   (Followers: 14)
Computational Statistics & Data Analysis     Hybrid Journal   (Followers: 30)
Computer     Full-text available via subscription   (Followers: 99)
Computer Aided Surgery     Open Access   (Followers: 6)
Computer Applications in Engineering Education     Hybrid Journal   (Followers: 8)
Computer Communications     Hybrid Journal   (Followers: 16)
Computer Journal     Hybrid Journal   (Followers: 9)

        1 2 3 4 5 6 7 | Last

Journal Cover
Automatica
Journal Prestige (SJR): 3.896
Citation Impact (citeScore): 7
Number of Followers: 13  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0005-1098
Published by Elsevier Homepage  [3162 journals]
  • K -step+opacity+of+stochastic+discrete-event+systems&rft.title=Automatica&rft.issn=0005-1098&rft.date=&rft.volume=">Infinite-step opacity and K -step opacity of stochastic discrete-event
           systems
    • Abstract: Publication date: January 2019Source: Automatica, Volume 99Author(s): Xiang Yin, Zhaojian Li, Weilin Wang, Shaoyuan Li Opacity is an important information-flow property that arises in security and privacy analysis of cyber–physical systems. Among many different notions of opacity, K-step opacity requires that the intruder can never determine unambiguously that the system was at a secret state for any specific instant within K steps prior to that particular instant. This notion becomes infinity-step opacity when K goes to infinity. Existing works on the analysis of infinite-step opacity and K-step opacity only provide a binary characterization, i.e., a system is either opaque or non-opaque. To analyze infinite-step and K-step opacity more quantitatively, in this paper, we investigate the verification of infinite-step and K-step opacity in the context of stochastic discrete-event systems. A new notion of opacity, called almost infinite-step opacity (respectively, almost K-step opacity), is proposed to capture whether or not the probability of violating infinite-step opacity (respectively, K-step opacity) is smaller than a given threshold. We also provide effective algorithms for the verification of almost infinite-step opacity and almost K-step opacity.
       
  • Distributed Kalman filtering for time-varying discrete sequential systems
    • Abstract: Publication date: January 2019Source: Automatica, Volume 99Author(s): Bo Chen, Guoqiang Hu, Daniel W.C. Ho, Li Yu Discrete sequential system (DSS) consisting of different dynamical subsystems is a sequentially-connected dynamical system, and has found applications in many fields such as automation processes and series systems. However, few results are focused on the state estimation of DSSs. In this paper, the distributed Kalman filtering problem is studied for time-varying DSSs with Gaussian white noises. A locally optimal distributed estimator is designed in the linear minimum variance sense, and a stability condition is derived such that the mean square error of the distributed estimator is bounded. An illustrative example is given to demonstrate the effectiveness of the proposed methods.
       
  • Reinforcement learning for a class of continuous-time input constrained
           optimal control problems
    • Abstract: Publication date: January 2019Source: Automatica, Volume 99Author(s): Farnaz Adib Yaghmaie, David J. Braun In this paper, we identify a class of input constrained optimal control problems which can be approximately solved using Reinforcement Learning (RL) approaches. We start with a general class of problems which do not admit the theoretical assumptions used to derive RL frameworks. We then restrict this class by extra conditions on the dynamics and the objective function as deemed necessary. Our attention concerns two assumptions: (i) the smoothness of the value function which is typically not satisfied in input constrained problems, and (ii) the form of the objective function which can be more general than what has been proposed in previous formulations. For the first assumption, we use the method of vanishing viscosity to derive the conditions under which RL approaches can be used to find an approximate solution. These conditions relax a differentiability assumption to a continuity assumption of the value function thereby extending the applicability of RL frameworks. For the second assumption, we generalize the specific integrand form of the control cost used in previous formations to a more general class of cost functions that guarantee continuity of the control policy. Using these results, we present a new partially model-free RL framework for optimal control of input constrained continuous-time systems. Our RL framework requires an initial stabilizing policy and guarantees uniformly ultimate boundedness of the state variables. We demonstrate our results by simulation examples.
       
  • Networked stabilization of multi-input systems over shared channels with
           scheduling/control co-design
    • Abstract: Publication date: January 2019Source: Automatica, Volume 99Author(s): Wei Chen, Jing Yao, Li Qiu In this paper, we study the networked stabilization of a continuous-time multi-input system wherein the multiple control inputs are transmitted through a small number of shared channels with stochastic multiplicative uncertainties. Transmission scheduling over the shared channels needs to be performed so that at any time instant, each channel transmits only one control input. The main novelty of this work lies in the idea of scheduling/control co-design which suggests that the design of the transmission scheduling and the controller should be treated jointly rather than separately. By virtue of such a co-design, a sufficient condition is obtained for the channels’ overall quality of service required for stabilization given in terms of twice of the topological entropy of the plant. A numerical example is provided to illustrate our result.
       
  • An interpretation of the Schrödinger equation in quantum mechanics from
           the control-theoretic point of view
    • Abstract: Publication date: January 2019Source: Automatica, Volume 99Author(s): Akira Ohsumi In this paper, for a given (time-dependent) Schrödinger equation in quantum mechanics, an interpretation of it is investigated from the perspective of stochastic control theory with the help of Nelson stochastic mechanics.
       
  • Observer-based consensus for second-order multi-agent systems with
           arbitrary asynchronous and aperiodic sampling periods
    • Abstract: Publication date: January 2019Source: Automatica, Volume 99Author(s): Tomas Ménard, Emmanuel Moulay, Patrick Coirault, Michael Defoort A novel distributed consensus protocol, where only sampled position information is exchanged between neighboring agents, is designed for second-order multi-agent systems under a directed communication topology. This protocol allows to reach the consensus for asynchronous and aperiodic sampling periods, which means that every agent can send its measurements independently from its neighbors. Furthermore, the upper bound on the sampling periods can be chosen arbitrarily long by adapting the tuning parameters. This result is obtained by using a continuous–discrete time observer which allows to reconstruct the system state in real time from only discrete-time measurements. The feedback control gain is set according to the observer gain which is itself set according to the maximum sampling period.
       
  • Detectability and observer design for switched
           differential–algebraic equations
    • Abstract: Publication date: January 2019Source: Automatica, Volume 99Author(s): Aneel Tanwani, Stephan Trenn This paper studies detectability for switched linear differential–algebraic equations (DAEs) and its application to the synthesis of observers, which generate asymptotically converging state estimates. Equating detectability to asymptotic stability of zero-output-constrained state trajectories, and building on our work on interval-wise observability, we propose the notion of interval-wise detectability: If the output of the system is constrained to be identically zero over an interval, then the norm of the corresponding state trajectories scales down by a certain factor at the end of that interval. Conditions are provided under which the interval-wise detectability leads to asymptotic stability of zero-output-constrained state trajectories. An application is demonstrated in designing state estimators. Decomposing the state into observable and unobservable components, we show that if the observable component of the system is reset appropriately and persistently, then the estimation error converges to zero asymptotically under the interval-wise detectability assumption.
       
  • Ensuring privacy with constrained additive noise by minimizing Fisher
           information
    • Abstract: Publication date: January 2019Source: Automatica, Volume 99Author(s): Farhad Farokhi, Henrik Sandberg The problem of preserving the privacy of individual entries of a database when responding to linear or nonlinear queries with constrained additive noise is considered. For privacy protection, the response to the query is systematically corrupted with an additive random noise whose support is a subset or equal to a pre-defined constraint set. A measure of privacy using the inverse of the trace of the Fisher information matrix is developed. The Cramér–Rao bound relates the variance of any estimator of the database entries to the introduced privacy measure. The probability density that minimizes the trace of the Fisher information (as a proxy for maximizing the measure of privacy) is computed. An extension to dynamic problems is also presented. Finally, the results are compared to the differential privacy methodology.
       
  • Robust event-triggered state estimation: A risk-sensitive approach
    • Abstract: Publication date: January 2019Source: Automatica, Volume 99Author(s): Jiarao Huang, Dawei Shi, Tongwen Chen In this work, we investigate a robust event-triggered remote state estimation problem for linear Gaussian systems with a stochastic event-triggering condition. The reference measure approach is used to obtain a robust event-triggered estimate that minimizes the so-called risk-sensitive criterion, which refers to the expectation of the exponential of the sum of the squared estimation error. We introduce the reference measure, under which, the measurements are identically independently distributed (i.i.d.) and independent of the states, and propose a map to link the “real-world” measure to the reference measure so that the recursions of the information states under the reference measure can be obtained. Based on these results, the risk-sensitive criteria are reformulated under the reference measure and closed-form expressions of the risk-sensitive event-triggered posterior and prior estimates are presented, which are shown to evolve in simple recursive Kalman-like structures. Moreover, two sufficient stability conditions for the proposed estimators are given, where the first requires the solution of a time-varying Riccati equation to be positive-definite and satisfy a specific inequality, which can be further extended to the scenario when the weighting matrix in the risk-sensitive criterion is time-variant; the second gives the range of values of the risk-sensitive parameter and covariance of the initial state for which the proposed estimators are stable. Comparative simulation results demonstrate that the proposed risk-sensitive event-triggered estimator is more robust to model uncertainties compared with a typical minimum mean squared error (MMSE) estimator with stochastic event-triggered sensor scheduling.
       
  • Distributed estimation based on multi-hop subspace decomposition
    • Abstract: Publication date: January 2019Source: Automatica, Volume 99Author(s): Álvaro Rodríguez del Nozal, Pablo Millán, Luis Orihuela, Alexandre Seuret, Luca Zaccarian This paper deals with the problem of distributedly estimating the state of an LTI plant through an interconnected network of agents. The proposed approach results in an observer structure that incorporates consensus among the agents and that can be distributedly designed, achieving a robust solution with a good estimation performance. The developed solution is based on an iterative decomposition of the plant in the local observable staircase forms. The proposed observer has several positive features compared to recent results in the literature, which include milder assumptions on the network connectivity and the ability to set the convergence rate.
       
  • Distributed algorithms for aggregative games of multiple heterogeneous
           Euler–Lagrange systems
    • Abstract: Publication date: January 2019Source: Automatica, Volume 99Author(s): Zhenhua Deng, Shu Liang In this paper, an aggregative game of Euler–Lagrange (EL) systems is investigated, where the cost functions of all players depend on not only their own decisions but also the aggregate of all decisions. Two distributed algorithms are designed for these heterogeneous EL players to reach the Nash equilibrium of aggregative games. By constructing suitable Lyapunov functions, the convergence of the two algorithms are analyzed. The first algorithm achieves globally exponential convergence without parameter uncertainty, and the other achieves globally asymptotic convergence, even in the presence of uncertain parameters. Numerical examples are given to illustrate the effectiveness of the methods.
       
  • On robust Kalman filter for two-dimensional uncertain linear discrete
           time-varying systems: A least squares method
    • Abstract: Publication date: January 2019Source: Automatica, Volume 99Author(s): Dong Zhao, Steven X. Ding, Hamid Reza Karimi, Yueyang Li, Youqing Wang The robust Kalman filter design problem for two-dimensional uncertain linear discrete time-varying systems with stochastic noises is investigated in this study. First, we prove that the solution to a certain deterministic regularized least squares problem constrained by the nominal two-dimensional system model is equivalent to the generalized two-dimensional Kalman filter. Then, based on this relationship, the robust state estimation problem for two-dimensional uncertain systems with stochastic noises is interpreted as a deterministic robust regularized least squares problem subject to two-dimensional dynamic constraint. Finally, by solving the robust regularized least squares problem and using a simple approximation, a recursive robust two-dimensional Kalman filter is determined. A heat transfer process serves as an example to show the properties and efficacy of the proposed filter.
       
  • Stability analysis of a system coupled to a heat equation
    • Abstract: Publication date: January 2019Source: Automatica, Volume 99Author(s): Lucie Baudouin, Alexandre Seuret, Frédéric Gouaisbaut As a first approach to the study of systems coupling finite and infinite dimensional natures, this article addresses the stability of a system of ordinary differential equations coupled with a classic heat equation using a Lyapunov functional technique. Inspired from recent developments in the area of time delay systems, a new methodology to study the stability of such a class of distributed parameter systems is presented here. The idea is to use a polynomial approximation of the infinite dimensional state of the heat equation in order to build an enriched energy functional. A well known efficient integral inequality (Bessel inequality) will allow to obtain stability conditions expressed in terms of linear matrix inequalities. We will eventually test our approach on academic examples in order to illustrate the efficiency of our theoretical results.
       
  • Generalization of a Result of Fabian on the Asymptotic Normality of
           Stochastic Approximation
    • Abstract: Publication date: Available online 10 November 2018Source: AutomaticaAuthor(s): Karla Hernández, James C. Spall Stochastic approximation (SA) is a general framework for analyzing the convergence of a large collection of stochastic root-finding algorithms. The Kiefer–Wolfowitz and stochastic gradient algorithms are two well-known (and widely used) examples of SA. Because of their applicability to a wide range of problems, many results have been obtained regarding the convergence properties of SA procedures. One important reference in the literature, Fabian (1968), derives general conditions for the asymptotic normality of the SA iterates. Since then, many results regarding asymptotic normality of SA procedures have relied heavily on Fabian’s theorem. Unfortunately, some of the assumptions of Fabian’s result are not applicable to some modern implementations of SA in control and learning. In this paper we explain the nature of this incompatibility and show how Fabian’s theorem can be generalized to address the issue.
       
  • On periodic optimal solutions of persistent sensor planning for
           continuous-time linear systems
    • Abstract: Publication date: January 2019Source: Automatica, Volume 99Author(s): Jung-Su Ha, Han-Lim Choi This work investigates informative planning of sensing agents over infinite time horizon when the system of interest is expressed as a continuous-time linear system. The objective of this planning problem, termed persistent monitoring problem, is to maintain the monitoring uncertainty at the minimum. The method reduces the persistent planning problem into a periodic planning problem; it is formulated as a periodic optimal control or optimization problem to determine the optimal periodic sensor plan as well as the period. The plan induces the periodic Riccati equation and is proven to lead an arbitrary initial uncertainty state to the optimized periodic trajectory. It is also proven that any infinite-horizon (non-periodic) sensor plan is able to be approximated arbitrary well by a periodic sensor plan. A suboptimal filtering mechanism is proposed by using the resulting optimal periodic solution. Two numerical examples on (a) a relaxed periodic sensor scheduling for a two dimensional linear system, and (b) persistent monitoring by a mobile sensor of two-dimensional diffusion dynamics show the validity of the proposed approach.
       
  • Design of super-twisting control gains: A describing function based
           methodology
    • Abstract: Publication date: January 2019Source: Automatica, Volume 99Author(s): Ulises Pérez-Ventura, Leonid Fridman The Describing Function approach is used to adjust the parameters of fast-oscillations (chattering) caused by the presence of fast-actuators in Super-Twisting control loops. Estimated parameters, amplitude and frequency of self-excited oscillations, allow to compute the average power needed to maintain the trajectories of the system into real sliding-modes. Through the parametrization of the actuator dynamics by a critically damped second-order system or by a constant delay, sets of STA gains are provided to minimize the amplitude of oscillations or the average power. The results are confirmed by simulations.
       
  • Switching and information exchange in compressed estimation of coupled
           high dimensional processes
    • Abstract: Publication date: January 2019Source: Automatica, Volume 99Author(s): Karan Narula, Jose E. Guivant Compressed Estimation approaches, such as the Generalised Compressed Kalman Filter (GCKF), reduce the computational cost and complexity of high-dimensional and high-frequency data assimilation problems, usually without sacrificing optimality. Configured using adequate cores, such as the Unscented Kalman Filter (UKF), the GCKF could also treat certain high-dimensional non-linear cases. However, the application of a compressed estimation process is limited to a class of problems which inherently allow the estimation process to be divided, at certain intervals of time, into a set of lower-dimensional problems. This limitation prohibits applying the compressing techniques for estimating coupled high-dimensional processes. However, those limitations can be overcome by applying proper techniques. In this paper, the concepts of subsystem switching and information exchange architecture, namely ‘Exploiting Local Statistical Dependency’ (ELSD), have been derived and explored, allowing compressed estimators to mimic optimal full-Gaussian estimators. The performances of the methods have been verified through applications in solving usual types of Stochastic Partial Differential Equations (SPDEs). The computational advantages of using the proposed techniques have also been highlighted with a recommendation for its usage over the full filter when dealing with high-dimensional and high-frequency data assimilation.
       
  • Decentralised estimation with correlation limited by optimal processing of
           independent data
    • Abstract: Publication date: January 2019Source: Automatica, Volume 99Author(s): Jiří Ajgl, Ondřej Straka Decentralised estimation aims at providing the best combination of multiple estimates. Since the exact solutions are expensive in terms of computation and communication requirements, the mean square error optimality is traded for the bound optimality. Fusion under unknown correlations has been inspected for two decades and the research now focuses on a partial knowledge of the correlations. This paper focuses on the assumption that the estimates to be fused were obtained by the optimal processing of local data with independent measurement errors. A generalisation of a recent solution to such a problem is proposed. In particular, non-uniqueness of the optimal fusion weights is discovered. The relation of the generalised and existing solutions is discussed and illustrative examples are given.
       
  • Innovative fractional derivative estimation of the pseudo-state for a
           class of fractional order linear systems
    • Abstract: Publication date: January 2019Source: Automatica, Volume 99Author(s): Yan-Qiao Wei, Da-Yan Liu, Driss Boutat In this paper, a non-asymptotic and robust method is proposed to estimate the fractional integral and derivative of the pseudo-state for a class of fractional order linear systems in noisy environment with unknown initial conditions. To the best of our knowledge, no method has been developed for such estimation. Firstly, the estimation problem is transformed into estimating the fractional integral and derivative of the output and a set of fractional derivative initial values. Then, algebraic integral formulas are exactly derived for the sought estimators by applying different modulating functions with specified properties. In particular, a design parameter is introduced in the formulas of fractional derivative initial values, which can improve the robustness by choosing appropriate values. Secondly, it is shown how to construct the required modulating functions in an efficient way, where another design parameter is involved. Moreover, some error analysis is given to choose the design parameters. Finally, numerical simulations are provided to demonstrate the efficiency and the robustness to noises of the proposed method.
       
  • Dynamics of semilattice networks with strongly connected dependency graph
    • Abstract: Publication date: January 2019Source: Automatica, Volume 99Author(s): Alan Veliz-Cuba, Reinhard Laubenbacher Discrete-time dynamical systems on a finite state space have been used to model natural and engineered systems such as biological networks, social networks, and engineered control systems. They have the advantage of being intuitive and the models can be easily simulated on a computer in most cases; however, few analytical tools beyond simulation are available. The motivation for this paper is to develop such tools. It identifies a broad class of discrete dynamical systems with a finite phase space for which one can derive strong results about their long-term dynamics in terms of properties of their dependency graphs. The paper contains a complete classification of the periodic orbits of semilattice networks with strongly connected dependency graph, by finding analytically the exact number of periodic orbits of any given period.
       
  • Output feedback control for unknown LTI systems driven by unknown periodic
           disturbances
    • Abstract: Publication date: January 2019Source: Automatica, Volume 99Author(s): Cemal Tugrul Yilmaz, Halil Ibrahim Basturk This paper considers unknown minimum-phase LTI systems with known relative degree and system order. The main aim is to reject the unknown, unmatched sinusoidal disturbances and make the output track a given trajectory with the output feedback. The essence of the control design is composed of disturbance parametrization, K-filter technique and adaptive backstepping procedure. Firstly, the unmeasured system states are represented in terms of filtered input and output signals. Then, the disturbance information in the output signal is parametrized and the problem is converted to an adaptive control problem. After that, the K-filter approach is employed to redefine the system states that enable to use a backstepping technique. An adaptive output feedback controller is designed recursively. It is proven that the equilibrium at the origin is globally uniformly stable and the output signal tracks the reference signal asymptotically. Finally, the effectiveness of the controller is illustrated with a numerical simulation. The robustness of the closed loop system with respect to an additive unmodelled noise is also discussed.
       
  • Sampled-data emulation of dynamic output feedback controllers for
           nonlinear time-delay systems
    • Abstract: Publication date: January 2019Source: Automatica, Volume 99Author(s): Mario Di Ferdinando, Pierdomenico Pepe In this paper we deal with the problem of the stabilization in the sample-and-hold sense by emulation of continuous-time dynamic output feedback controllers. Nonlinear time-delay systems not necessarily affine in the control input are studied. Sufficient conditions are provided such that the emulation of continuous-time dynamic output feedback controllers yields stabilization in the sample-and-hold sense. The inter-sampling system behavior as well as time-varying sampling intervals are taken into account. The case of nonlinear delay-free systems is addressed as a special case. In this case, it is shown that the sufficient conditions, ensuring the stabilization in the sample-and-hold sense, are satisfied if the continuous-time dynamic output feedback controller is a global stabilizer. An example is presented which validates the theoretical results.
       
  • Analysis and synthesis for a class of stochastic switching systems against
           delayed mode switching: A framework of integrating mode weights
    • Abstract: Publication date: January 2019Source: Automatica, Volume 99Author(s): Lixian Zhang, Zepeng Ning, Yang Shi This paper is concerned with the issues of stability analysis and control synthesis for a class of linear stochastic switching systems in discrete-time domain. The switching dynamics are considered to be governed by a semi-Markov process and the sojourn time for each system mode is deemed to be finite. A mode-dependent control scheme is employed with an adaptation sense in the presence of time delays in the mode switching of controller, which is manifested as a constant lag between the system mode and the controller mode. A novel form of Lyapunov function is adopted, in which the Lyapunov matrix depends on the modes of both the system and the controller as well as the time since the occurrence of the last mode switching. On the basis of the new proposed σ-error mean-square stability that integrates the weights of all the system modes, numerically testable stability criteria are developed via the semi-Markov kernel approach. In virtue of certain techniques that can eliminate the terms containing powers or products of matrices, a desired mode-dependent stabilizing controller is designed such that the closed-loop system is σ-error mean-square stable by allowing a mode-unmatched controller to perform before the controller switches to a mode-matched one. Finally, the theoretical results are applied to a practical example of one joint of a space robot manipulator to demonstrate the effectiveness, applicability and superiority of the proposed control strategy as well as the necessity of considering the mode-switching delays in the designed controller.
       
  • Consensus conditions of continuous-time multi-agent systems with
           time-delays and measurement noises
    • Abstract: Publication date: Available online 4 November 2018Source: AutomaticaAuthor(s): Xiaofeng Zong, Tao Li, Ji-Feng Zhang This work is concerned with stochastic consensus conditions of multi-agent systems with both time-delays and measurement noises. For the case of additive noises, we develop some necessary conditions and sufficient conditions for stochastic weak consensus by estimating the differential resolvent function for delay equations. By the martingale convergence theorem, we obtain necessary conditions and sufficient conditions for stochastic strong consensus. For the case of multiplicative noises, we consider two kinds of time-delays, appeared in the measurement term and the noise term, respectively. We first show that stochastic weak consensus with the exponential convergence rate implies stochastic strong consensus. Then by constructing degenerate Lyapunov functional, we find the sufficient consensus conditions and show that stochastic consensus can be achieved by carefully choosing the control gain according to the noise intensities and the time-delay in the measurement term.
       
  • Event-triggered distributed predictive control for asynchronous
           coordination of multi-agent systems
    • Abstract: Publication date: January 2019Source: Automatica, Volume 99Author(s): Yuanyuan Zou, Xu Su, Shaoyuan Li, Yugang Niu, Dewei Li This paper investigates the event-triggered distributed predictive control (DPC) problem for multi-agent systems subject to bounded disturbances. A novel event-triggering mechanism which involves the neighbours’ information is derived for each agent to achieve a trade-off between resource usage and control performance. In such a framework, the DPC optimization problem is solved and information is exchanged only at triggering instants, thus achieving asynchronous coordination. To lower computation and communication consumption more significantly, a dynamic variable considering effects of neighbours is introduced to design a dynamic event-triggering condition and we show that larger inter-execution time can be obtained using the dynamic triggering mechanism. The theoretical conditions on ensuring feasibility and closed-loop stability are developed for these two triggering mechanisms, respectively. Finally, numerical simulations are given to illustrate the effectiveness of the proposed control strategy.
       
  • Coordinated trajectory tracking of multiple vertical take-off and landing
           UAVs
    • Abstract: Publication date: January 2019Source: Automatica, Volume 99Author(s): Yao Zou, Ziyang Meng This paper investigates the coordinated trajectory tracking problem of multiple vertical take-off and landing (VTOL) unmanned aerial vehicles (UAVs). The case of unidirectional information flow is considered and the objective is to drive all the follower VTOL UAVs to accurately track a desired trajectory associated with a leader. Firstly, a novel distributed estimator is developed for each VTOL UAV to obtain the leader’s desired information asymptotically. With the outputs of the estimators, the solution to the coordinated trajectory tracking problem of multiple VTOL UAVs is transformed to individually solving the tracking problem of each VTOL UAV. Due to the under-actuated nature of the VTOL UAV, a hierarchical framework is introduced for each VTOL UAV such that a command force and an applied torque are exploited in sequence, then the position tracking to the estimated desired position and the attitude tracking to the command attitude are achieved. Moreover, an auxiliary system with proper parameters is implemented to guarantee the singularity-free command attitude extraction and to obviate the use of the unavailable desired information. The stability analysis and simulations effectively validate the achievement of the coordinated trajectory tracking of multiple VTOL UAVs with the proposed control approach.
       
  • Adaptive stabilization of switched affine systems with unknown equilibrium
           points: Application to power converters
    • Abstract: Publication date: January 2019Source: Automatica, Volume 99Author(s): Gaëtan Beneux, Pierre Riedinger, Jamal Daafouz, Louis Grimaud The paper addresses the problem of designing a stabilizing control for switched affine systems with unknown parameters. We formulate the problem both in the case where the set of affine subsystems is finite and also in the case where the set of affine subsystems is not finite and given by a convex polytope, i.e., the convex hull of finitely many affine subsystems. The main contribution is a switched and adaptive control design methodology with a global asymptotic stability property. The difficulty is related to the fact that the equilibrium point is unknown a priori. We propose an observer-based control strategy that uses a parameter estimate to update the control law in real time. A DC/DC Flyback converter is considered to illustrate the effectiveness of the proposed method. We also show that the proposed strategy preserves the stability property when the Flyback converter works in the so-called discontinuous conduction mode (DCM).
       
  • Multi-stage discrete time and randomized dynamic average consensus
    • Abstract: Publication date: January 2019Source: Automatica, Volume 99Author(s): Mauro Franceschelli, Andrea Gasparri In this paper we propose a novel local interaction protocol which solves the discrete time dynamic average consensus problem, i.e., the consensus problem on the average value of a set of time-varying input signals in an undirected graph. The proposed interaction protocol is based on a multi-stage cascade of dynamic consensus filters which tracks the average value of the inputs with small and bounded error. We characterize its convergence properties for time-varying discrete-time inputs with bounded variations. The main novelty of the proposed algorithm is that, with respect to other dynamic average consensus protocols, we obtain the next unique set of advantages: i) The protocol, inspired by proportional dynamic consensus, does not exploit integral control actions or input derivatives, thus exhibits robustness to re-initialization errors, changes in the network size and noise in the input signals; ii) The proposed design allows to trade-off the quantity of information locally exchanged by the agents, i.e., the number of stages, with steady-state error, tracking error and convergence time; iii) The protocol can be implemented with randomized and asynchronous local state updates and keep in expectation the performance of the discrete-time version. Numerical examples are given to corroborate the theoretical findings, including the case where a new agent joins and leaves the network during the algorithm execution to show robustness to re-initialization errors during runtime.
       
  • An exponential quantum projection filter for open quantum systems
    • Abstract: Publication date: January 2019Source: Automatica, Volume 99Author(s): Qing Gao, Guofeng Zhang, Ian R. Petersen An approximate exponential quantum projection filtering scheme is developed for a class of open quantum systems described by Hudson–Parthasarathy quantum stochastic differential equations, aiming to reduce the computational burden associated with online calculation of the quantum filter. By using a differential geometric approach, the quantum trajectory is constrained in a finite-dimensional differentiable manifold consisting of an unnormalized exponential family of quantum density operators, and an exponential quantum projection filter is then formulated as a number of stochastic differential equations satisfied by the finite-dimensional coordinate system of this manifold. A convenient design of the differentiable manifold is also presented through reduction of the local approximation errors, which yields a simplification of the quantum projection filter equations. It is shown that the computational cost can be significantly reduced by using the quantum projection filter instead of the quantum filter. It is also shown that when the quantum projection filtering approach is applied to a class of open quantum systems that asymptotically converge to a pure state, the input-to-state stability of the corresponding exponential quantum projection filter can be established. Simulation results from an atomic ensemble system example are provided to illustrate the performance of the projection filtering scheme. It is expected that the proposed approach can be used in developing more efficient quantum control methods.
       
  • Hybrid mechanisms for robust synchronization and coordination of
           multi-agent networked sampled-data systems
    • Abstract: Publication date: January 2019Source: Automatica, Volume 99Author(s): Jorge I. Poveda, Andrew R. Teel We present a novel approach for the design of robust feedback coordination and control mechanisms for networks of asynchronous nonlinear multi-agent systems (MAS). Each agent corresponds to a sampled-data system characterized by a continuous-time plant, a discrete-time controller with logic or integer states, and a sampler/zero-order hold with a local clock. The goal is to robustly stabilize an application-dependent compact set defined a priori for the MAS, taking into account the asynchronous nature of the triggering mechanisms of the agents, and the limited information in the network. To solve this problem, we propose an emulation-like approach, where the feedback mechanism for each agent is initially designed for a nominal ideal synchronous MAS with a single logic state. Unlike existing emulation results for networked sampled-data systems with a single triggering mechanism, the implementation of multiple triggering mechanisms in MAS requires additional decentralized coordination algorithms to guarantee that the implemented system robustly emulates the behavior of the nominal synchronous system. Therefore, we propose a decentralized synchronization and coordination mechanism that controls the triggering mechanisms of the agents, guaranteeing robust stabilization of the closed-loop MAS. Our results are established by using Lyapunov tools, the invariance principle, and robustness corollaries for set-valued hybrid dynamical systems.
       
  • Subspace identification with moment matching
    • Abstract: Publication date: January 2019Source: Automatica, Volume 99Author(s): Masaki Inoue We propose a deterministic identification method that involves a priori information characterized as moments of a transfer function. The moments are expressed in terms of the solution to a Sylvester matrix equation. The Sylvester equation is incorporated with a conventional subspace identification method, and a problem for moment-constrained subspace identification is formulated. Since the identification problem is in a class of nonlinear optimization problems, it cannot be efficiently solved in numerical computation. Application of a change-of-variable technique reduces the problem to least squares optimization, and the solution provides a state-space model that involves the prespecified moments. Finally, the effectiveness of the proposed method is illustrated in numerical simulations.
       
  • Time-optimal hands-off control for linear time-invariant systems
    • Abstract: Publication date: January 2019Source: Automatica, Volume 99Author(s): Takuya Ikeda, Masaaki Nagahara In this article, we investigate theoretical properties of the time-optimal hands-off control for linear time-invariant systems. The purpose of the control is to maximize the time duration on which the control value is exactly zero (maximum hands-off control) and also to minimize the response time to achieve a given state transition (time-optimal control). For this, we introduce a cost function described by a linear combination of the L0 measure and the response time of the control. Since the L0 measure is non-convex and discontinuous, we adopt the L1 relaxation technique for the analysis of the optimal control. By using this relaxation, we show the existence of the time-optimal hands-off control, and the equivalence between L0 andL1 optimal controls under the normality assumption.
       
  • Comments on “Adaptive tracking control of uncertain MIMO nonlinear
           systems with input constraints”
    • Abstract: Publication date: Available online 31 October 2018Source: AutomaticaAuthor(s): An-Min Zou, Anton H.J. de Ruiter, Krishna Dev Kumar
       
  • Stabilization of a class of slow–fast control systems at
           non-hyperbolic points
    • Abstract: Publication date: January 2019Source: Automatica, Volume 99Author(s): Hildeberto Jardón-Kojakhmetov, Jacquelien M.A. Scherpen, Dunstano del Puerto-Flores In this document, we deal with the local asymptotic stabilization problem of a class of slow–fastsystems (or singularly perturbed Ordinary Differential Equations). The systems studied here have the following properties: (1) they have one fast and an arbitrary number of slow variables, and (2) they have a non-hyperbolic singularity at the origin of arbitrary degeneracy. Our goal is to stabilize such a point. The presence of the aforementioned singularity complicates the analysis and the controller design. In particular, the classical theory of singular perturbations cannot be used. We propose a novel design based on geometric desingularization, which allows the stabilization of a non-hyperbolic point of singularly perturbed control systems. Our results are exemplified on a didactic example and on an electric circuit.
       
  • Stochastic learning in multi-agent optimization: Communication and
           payoff-based approaches
    • Abstract: Publication date: January 2019Source: Automatica, Volume 99Author(s): Tatiana Tatarenko Game theory serves as a powerful tool for distributed optimization in multi-agent systems in different applications. In this paper we consider multi-agent systems that can be modeled by means of potential games whose potential function coincides with a global objective function to be maximized. In this approach, the agents correspond to the strategic decision makers and the optimization problem is equivalent to the problem of learning a potential function maximizer in the designed game. The paper deals with two different information settings in the system. Firstly, we consider systems, where agents have the access to the gradient of their utility functions. However, they do not possess the full information about the joint actions. Thus, to be able to move along the gradient toward a local optimum, they need to exchange the information with their neighbors by means of communication. The second setting refers to a payoff-based approach. Here, we assume that at each iteration agents can only observe their own played actions and experienced payoffs. In both cases, the paper studies unconstrained non-concave optimization with a differentiable objective function. To develop the corresponding algorithms guaranteeing convergence to a local maximum of the potential function in absence of saddle points, we utilize the idea of the well-known Robbins–Monro procedure based on the theory of stochastic approximation.
       
  • On the construction of safe controllable regions for affine systems with
           applications to robotics
    • Abstract: Publication date: December 2018Source: Automatica, Volume 98Author(s): Mohamed K. Helwa, Angela P. Schoellig This paper studies the problem of constructing in-block controllable (IBC) regions for affine systems. That is, we are concerned with constructing regions in the state space of affine systems such that all the states in the interior of the region are mutually accessible within the region’s interior by applying uniformly bounded inputs. We first show that existing results for checking in-block controllability on given polytopic regions cannot be easily extended to address the question of constructing IBC regions. We then explore the geometry of the problem to provide a computationally efficient algorithm for constructing IBC regions. We also prove the soundness of the algorithm. We then use the proposed algorithm to construct safe speed profiles for robotic systems. As a case study, we present several experimental results on unmanned aerial vehicles (UAVs) to verify the effectiveness of the proposed algorithm; these results include using the proposed algorithm for real-time collision avoidance for UAVs.
       
  • H +filtering+for+discrete-time+2-D+switched+systems:+An+extended+average+dwell+time+approach&rft.title=Automatica&rft.issn=0005-1098&rft.date=&rft.volume=">H ∞ filtering for discrete-time 2-D switched systems: An extended
           average dwell time approach
    • Abstract: Publication date: December 2018Source: Automatica, Volume 98Author(s): Rongni Yang, Wei Xing Zheng This paper studies the H∞ filtering problem for a class of discrete-time two-dimensional (2-D) switched systems. The 2-D systems under consideration are described by the well-known Fornasini–Marchesini local state-space model. Our attention is focused on designing a full-order filter such that the filtering error system is guaranteed to be asymptotically or exponentially stable with a prescribed H∞ disturbance attenuation level. Based on the switched quadratic Lyapunov function and the piecewise Lyapunov function approach, sufficient conditions are established to ensure the existence of the desired filters for such systems under the arbitrary switching and restricted switching signals, respectively. Furthermore, the notion of time instant for 2-D switched systems is introduced and the extended average dwell time technique is utilized under the restricted switching signal. Then the corresponding filter is designed by dealing with a convex optimization problem that can be efficiently solved via standard numerical algorithms. Finally, two numerical examples are provided to demonstrate the effectiveness of the developed filter design algorithms.
       
  • Tracking control of uncertain nonlinear systems with deferred asymmetric
           time-varying full state constraints
    • Abstract: Publication date: December 2018Source: Automatica, Volume 98Author(s): Yong-Duan Song, Shuyan Zhou In this paper, we investigate the tracking control problem of uncertain strict-feedback systems under deferred and asymmetric yet time-varying (DATV) constraints. We show that such type of constraints, occurring some time after (rather than from the beginning of) system operation, are frequently encountered in practice that have not been adequately addressed in existing works. By utilizing an error-shifting transformation, together with a new asymmetric Barrier Lyapunov Function with variational barrier bounds, we develop a tracking control method capable of dealing with DATV full state constraints under completely unknown initial tracking condition, leading to a control solution to the underlying problem. We also show that, with the proposed method, full state constraints being violated initially (rendering the previous methods inapplicable) can be made satisfied within a pre-specified finite time. The benefits and effectiveness of the proposed control are theoretically authenticated and numerically validated.
       
  • Permanent magnet synchronous motors are globally asymptotically
           stabilizable with PI current control
    • Abstract: Publication date: December 2018Source: Automatica, Volume 98Author(s): Romeo Ortega, Nima Monshizadeh, Pooya Monshizadeh, Dmitry Bazylev, Anton Pyrkin This note shows that the industry standard desired equilibrium for permanent magnet synchronous motors (i.e, maximum torque per Ampere) can be globally asymptotically stabilized with a PI control around the current errors, provided some viscous friction (possibly small) is present in the rotor dynamics and the proportional gain of the PI is suitably chosen. Instrumental to establish this surprising result is the proof that the map from voltages to currents of the incremental model of the motor satisfies some passivity properties. The analysis relies on basic Lyapunov theory making the result available to a wide audience.
       
  • Adaptive output feedback control of stochastic nonholonomic systems with
           nonlinear parameterization
    • Abstract: Publication date: December 2018Source: Automatica, Volume 98Author(s): Hui Wang, Quanxin Zhu The problem of adaptive output-feedback control of nonlinearly parameterized stochastic nonholonomic systems is studied in this paper. Since many unknowns (e.g., unknown control coefficients and unknown nonlinear parameters in systems’ nonlinearities) occur into systems, we utilize an adaptive control method, together with a parameter separation technique, to construct an adaptive output feedback controller to regulate the whole systems. During the design procedure, a new form of reduced-order K-filters is given to compensate the unmeasured states of the systems. A switching strategy is proposed explicitly to stabilize the entire systems in the control scheme. Finally, a bilinear model with stochastic disturbances is presented to demonstrate our theoretical results.
       
  • Finite-time stabilization of weak solutions for a class of non-local
           Lipschitzian stochastic nonlinear systems with inverse dynamics
    • Abstract: Publication date: December 2018Source: Automatica, Volume 98Author(s): Gui-Hua Zhao, Jian-Chao Li, Shu-Jun Liu In this paper, finite-time stabilization is investigated for a class of non-local Lipschitzian stochastic nonlinear systems with stochastic inverse dynamics. Different from the existing work about finite-time control, to guarantee the existence of the solution under mild conditions, we study the stabilization in the sense of weak solution. We first present a finite-time stability theory under the framework of weak solution. Then, for a class of stochastic nonlinear systems with stochastic inverse dynamics, a finite-time controller via state feedback is constructively designed under the assumption that the stochastic inverse dynamics is stochastic input-to-state stable. The trivial weak solution of the closed-loop system is proved to be globally finite-time stable in probability. Finally, a simulation example is given to illustrate the efficiency of the proposed design procedure.
       
  • Asymptotic adaptive control of nonlinear systems with elimination of
           overparametrization in a Nussbaum-like design
    • Abstract: Publication date: December 2018Source: Automatica, Volume 98Author(s): Ci Chen, Zhi Liu, Kan Xie, Yun Zhang, C.L. Philip Chen Making a trade-off between the control accuracy and computational reduction is a problem frequently encountered in the system control design. This is especially difficult when one designs adaptive fuzzy (or neural network) controls for nonlinear systems, in which fuzzy controls have to consume many computational resources to tune a sufficiently large number of adaptive parameters, meanwhile nonlinear uncertainties block the high demanding control accuracy. Current works usually face a dilemma that, either the computation is reduced but the control accuracy is degraded due to the use of the norm estimation, or the asymptotic control is resulted but the computation is increased due to the extra compensation controls. To address such dilemma, we propose an asymptotic adaptive fuzzy tracking control algorithm, whose main feature is that only two adaptive laws are needed throughout the control scheme. In particular, we allocate one adaptive law to achieve adaptive fuzzy backstepping control for nonlinear systems with a focus on stabilizing the closed-loop system. We then allocate the other adaptive law not only to asymptotically drive the stabilization error to the zero, but also to achieve the elimination of overparametrization in a Nussbaum-like design, which is inspired by the tuning function-based approach.
       
  • An improved time-delay implementation of derivative-dependent feedback
    • Abstract: Publication date: December 2018Source: Automatica, Volume 98Author(s): Anton Selivanov, Emilia Fridman We consider an LTI system of relative degree r≥2 that can be stabilized using r−1 output derivatives. The derivatives are approximated by finite differences leading to a time-delayed feedback. We present a new method of designing and analyzing such feedback under continuous-time and sampled measurements. This method admits essentially larger time-delay/sampling period compared to the existing results and, for the first time, allows to use consecutively sampled measurements in the sampled-data case. The main idea is to present the difference between the derivative and its approximation in a convenient integral form. The kernel of this integral is hard to express explicitly but we show that it satisfies certain properties. These properties are employed to construct the Lyapunov–Krasovskii functional that leads to LMI-based stability conditions. If the derivative-dependent control exponentially stabilizes the system, then its time-delayed approximation stabilizes the system with the same decay rate provided the time-delay (for continuous-time measurements) or the sampling period (for sampled measurements) are small enough.
       
  • Adaptive asymptotic tracking using barrier functions
    • Abstract: Publication date: December 2018Source: Automatica, Volume 98Author(s): Yong-Hua Liu, Hongyi Li This paper studies the global output tracking problem for a class of unknown time-varying nonlinear systems in strict-feedback form. By utilizing the barrier functions, a universal adaptive state-feedback control strategy is proposed that achieves asymptotic tracking performance. Unlike the existing results in the literature, the proposed control scheme utilizes the barrier functions to ensure the unknown system nonlinearities to be the bounded “disturbance-like” terms, which are adaptively compensated at each step, this enables any approximation structures are not needed. Furthermore, the “explosion of complexity” issue in backstepping-like approaches is avoided without using additional filtering. Simulation results are presented to demonstrate the effectiveness of the proposed methodology.
       
  • Frequency domain identification of continuous-time output-error models
           with time-delay from relay feedback tests
    • Abstract: Publication date: December 2018Source: Automatica, Volume 98Author(s): Fengwei Chen, Hugues Garnier, Marion Gilson, Xiangtao Zhuan This paper is concerned with identification of continuous-time output-error models with time-delay from relay feedback tests. Conventional methods for solving this problem consist in deriving analytical limit cycle expressions and fitting them to measured shape factors. However, they may fail to handle different limit cycles uniformly, due to the structural differences in the analytical expressions. To overcome this problem, we consider a more general, data-based, parametric identification framework using sampled limit cycle data. A frequency domain method that minimizes the sum of squared output-errors is developed. The proposed method can be of high accuracy, thanks to the periodic input–output signals provided by sustained relay feedback oscillations, which can help to reduce leakage and aliasing errors. Besides, a distinctive merit of the proposed method is that identification of stable and unstable plants can be equally simple. The effectiveness and superiority of the proposed method are demonstrated by means of both theoretical analyses and simulation examples.
       
  • Prediction error identification of linear dynamic networks with
           rank-reduced noise
    • Abstract: Publication date: December 2018Source: Automatica, Volume 98Author(s): Harm H.M. Weerts, Paul M.J. Van den Hof, Arne G. Dankers Dynamic networks are interconnected dynamic systems with measured node signals and dynamic modules reflecting the links between the nodes. We address the problem of identifying a dynamic network with known topology, on the basis of measured signals, for the situation of additive process noise on the node signals that is spatially correlated and that is allowed to have a spectral density that is singular. A prediction error approach is followed in which all node signals in the network are jointly predicted. The resulting joint-direct identification method, generalizes the classical direct method for closed-loop identification to handle situations of mutually correlated noise on inputs and outputs. When applied to general dynamic networks with rank-reduced noise, it appears that the natural identification criterion becomes a weighted LS criterion that is subject to a constraint. This constrained criterion is shown to lead to maximum likelihood estimates of the dynamic network and therefore to minimum variance properties, reaching the Cramér–Rao lower bound in the case of Gaussian noise. In order to reduce technical complexity, the analysis is restricted to dynamic networks with strictly proper modules.
       
  • Balancing and suppression of oscillations of tension and cage in
           dual-cable mining elevators
    • Abstract: Publication date: December 2018Source: Automatica, Volume 98Author(s): Ji Wang, Yangjun Pi, Miroslav Krstic Dual-cable mining elevator has advantages in the transportation of heavy load to a large depth over the single cable elevator. However challenges occur when lifting a cage via two parallel compliant cables, such as tension oscillation inconformity between two cables and the cage roll, which are important physical variables relating to the fatigue fracture of mining cables. Mining elevator vibration dynamics are modeled by two pairs of 2 × 2 heterodirectional coupled hyperbolic PDEs on a time-varying domain and all four PDE bottom boundaries are coupled at one ODE. We design an output feedback boundary control law via backstepping to exponentially stabilize the dynamic system including the tension oscillation states, tension oscillation error states and the cage roll states. The control law is constructed with the estimated states from the observer formed by available boundary measurements. The exponential stability of the closed-loop system is proved via Lyapunov analysis. Effective suppression of tension oscillations, reduction of inconformity between tension oscillations in two cables, and balancing the cage roll under the proposed controller are verified via numerical simulation.
       
  • Cooperative and mobile manipulation of multiple microscopic objects based
           on micro-hands and laser-stage control
    • Abstract: Publication date: December 2018Source: Automatica, Volume 98Author(s): Quang Minh Ta, Chien Chern Cheah While various techniques have been developed for manipulation of biological cells or micro-objects using optical tweezers, the performance and feasibility of these techniques are mostly dependent on the physical properties of the target objects to be manipulated. In these existing techniques, direct trapping and manipulation of the manipulated objects using laser traps are performed, and therefore, existing techniques for optical manipulation are not capable of coordinating and manipulating various types of objects in the micro-world, including untrappable micro-objects, relatively large micro-objects, and laser sensitive biological cells. In this paper, a cooperative control technique is proposed for coordinative and mobile manipulation of multiple microscopic objects using micro-hands with multiple laser-driven fingertips and robot-assisted stage control. Several virtual micro-hands are formed by coordinating multiple optically trapped micro-particles that serve as the laser-driven fingertips, and then utilized for individual and coordinative manipulation of the target micro-objects. Simultaneously, global transportation of all the grasped target objects is performed by controlling the robot-assisted stage. While it is difficult to design multi-fingered hands in micro-scale due to scaling effect, this paper presents the first result on cooperative and mobile manipulation of multiple micro-objects using multiple micro-hands with laser-driven fingertips and robot-assisted stage control. In this paper, a primary study on repositioning strategy of the laser-driven fingertips is also introduced to allow the fingertips in a grasping formation to be repositioned. Rigorous mathematical formulations and solutions are derived to achieve the control objective, and experimental results are presented to demonstrate the effectiveness of the proposed control technique.
       
  • Decentralized control scheme for large-scale systems defined over a graph
           in presence of communication delays and random missing measurements
    • Abstract: Publication date: December 2018Source: Automatica, Volume 98Author(s): Yan Wang, Junlin Xiong, Daniel W.C. Ho This paper studies the decentralized output-feedback control of large-scale systems defined over a directed connected graph with communication delay and random missing measurements. The nodes in the graph represent the subsystems, and the edges represent the communication connection. The information travels across an edge in the graph and suffers from one step communication delay. For saving the storage space, the information delayed more than D step times is discarded. In addition, to model the system in a more practical case, we assume that the observation for the subsystem output suffers random missing. Under this new information pattern, the optimal output-feedback control problem is non-convex, what is worse, the separation principle fails. This implies that the optimal control problem with the information pattern introduced above is difficult to solve. In this paper, a new decentralized control scheme is proposed. In particular, a new estimator structure and a new controller structure are constructed, and the gains of the estimator and the controller are designed simultaneously. An optimality condition with respect to the gains is established. Based on the optimality condition, an iterative algorithm is exploited to design the gains numerically. It is shown that the exploited algorithm converges to Nash optimum. Finally, the proposed theoretical results are illustrated by a physical system which is a heavy duty vehicles platoon.
       
  • Low-power peaking-free high-gain observers
    • Abstract: Publication date: December 2018Source: Automatica, Volume 98Author(s): Daniele Astolfi, Lorenzo Marconi, Laurent Praly, Andrew R. Teel We propose a peaking-free low-power high-gain observer that preserves the main feature of standard high-gain observers in terms of arbitrarily fast convergence to zero of the estimation error, while overtaking their main drawbacks, namely the “peaking phenomenon” during the transient and the numerical implementation issue deriving from the high-gain parameter that is powered up to the order of the system. Moreover, the new observer is proved to have superior features in terms of sensitivity of the estimation error to high-frequency measurement noise when compared with standard high-gain observers. The proposed observer structure has a high-gain parameter that is powered just up to two regardless the dimension of the observed system and adopts saturations to prevent the peaking of the estimates during the transient. As for the classical solution, the new observer is robust with respect to uncertainties in the observed system dynamics in the sense that practical estimation in the high-gain parameter can be proved.
       
  • Low complexity constrained control using higher degree Lyapunov functions
    • Abstract: Publication date: December 2018Source: Automatica, Volume 98Author(s): Sarmad Munir, Morten Hovd, Sorin Olaru Explicit Model Predictive Control often has a complex solution in terms of the number of regions required to define the solution and the corresponding memory requirement to represent the solution in the online implementation. An alternative approach to constrained control is based on the use of controlled contractive sets. However, polytopic controlled contractive sets may themselves be relatively complex, leading to a complex explicit solution, and the polytopic structure can limit the size of the controlled contractive set. This paper develops a method to obtain a larger controlled contractive set by allowing higher order functions in the definition of the contractive set, and explores the use of such higher-order contractive sets in controller design leading to a low complexity explicit control formulation.
       
  • Dissipative control for nonlinear Markovian jump systems with actuator
           failures and mixed time-delays
    • Abstract: Publication date: Available online 25 September 2018Source: AutomaticaAuthor(s): Lifeng Ma, Zidong Wang, Qing-Long Han, Yurong Liu This paper addresses the dissipative control problem for nonlinear Markovian jump systems subject to actuator failures and mixed time-delays, where the mixed time-delays consist of both discrete and distributed time-delays and are mode-dependent. The purpose of the problem under investigation is to design a state feedback controller such that, in the presence of actuator failures and mixed time-delays, the closed-loop system is asymptotically stable in the mean square sense while achieving the pre-specified dissipativity. By constructing a Lyapunov–Krasovskii functional and using a completing square approach, sufficient conditions are proposed for the existence of the desired controller in terms of the solvability of certain Hamilton–Jacobi inequalities. Finally, an illustrative numerical example is provided to demonstrate the effectiveness of the developed control scheme.
       
  • Nonlinear state estimation under bounded noises
    • Abstract: Publication date: December 2018Source: Automatica, Volume 98Author(s): Bo Chen, Guoqiang Hu Most of the existing nonlinear state estimation methods require to know the statistical information of noises. However, the statistical information may not be accurately obtained or satisfied in practical applications. Actually, the noises are always bounded in a practical system. In this paper, we study the nonlinear state estimation problem under bounded noises, where the addressed noises do not provide any statistical information, and the bounds of noises are also unknown. By using matrix analysis and second-order Taylor series expansion, a novel constructive method is proposed to find an upper bound of the square error of the nonlinear estimator. Then, a convex optimization problem on the design of an optimal estimator gain is established in terms of linear matrix inequalities, which can be solved by standard software packages. Moreover, stability conditions are derived such that the square error of the designed nonlinear estimator is asymptotically bounded. Finally, two illustrative examples are employed to show the advantages and effectiveness of the proposed methods.
       
  • Collaborative operational fault tolerant control for stochastic
           distribution control system
    • Abstract: Publication date: December 2018Source: Automatica, Volume 98Author(s): Yuwei Ren, Yixian Fang, Aiping Wang, Huaxiang Zhang, Hong Wang Based on a class of industrial processes, a new distributed fault diagnosis approach and a collaborative operational fault tolerant control law are proposed for an irreversible interconnected stochastic distribution control (SDC) system with boundary conditions. This control method is different from the existing collaborative fault tolerant controllers which enable the output probability density function (PDF) to track a desired PDF as close as possible. When fault occurs, a setpoint redesigned fault tolerant approach is adopted to accommodate the fault instead of reconstructing the controller. An augmented PID nominal controller and a setpoint compensation item with linear structure are used to obtain a collaborative operational fault tolerant controller via solution of linear matrix inequalities (LMIs). Simulations are included to show the effectiveness of the proposed algorithms where encouraging results have been obtained.
       
  • Secure Luenberger-like observers for cyber–physical systems under sparse
           actuator and sensor attacks
    • Abstract: Publication date: December 2018Source: Automatica, Volume 98Author(s): An-Yang Lu, Guang-Hong Yang This paper investigates the secure state estimation problem for cyber–physical systems (CPSs) under sparse actuator and sensor attacks. By introducing the notion of orthogonal complement matrix, a necessary and sufficient condition for the state observability is provided. Then, based on the least square technique, a new projection operator is proposed to reconstruct the state from a set of successive measurements. Besides, by constructing an augmented system where the attacks are seen as part of the augmented state vector, a novel secure Luenberger-like observer is proposed, and sufficient conditions for the existence of the desired observer are proposed in terms of linear matrix inequalities (LMIs). It is shown that the proposed observability condition can be reduced to the sparse observability. A distinguishing point is that the attacks may be still unavailable even if the state is observable, and besides estimating the state, the attacks are also reconstructed by the proposed algorithm and observer according to their observability automatically.
       
  • Robust consensus of uncertain linear multi-agent systems via dynamic
           output feedback
    • Abstract: Publication date: December 2018Source: Automatica, Volume 98Author(s): Xianwei Li, Yeng Chai Soh, Lihua Xie This paper systematically deals with robust consensus of uncertain linear multi-agent systems via dynamic output-feedback protocols. Agents are assumed to have identical nominal linear time-invariant dynamics but are subject to heterogeneous additive stable perturbations. Dynamic output-feedback protocols with or without controller state information exchange between neighboring controllers are studied in a unified framework. Two methods are proposed for protocol design, which need to solve an algebraic Riccati equation and some scalar/matrix inequalities. The first method characterizes some key parameters by scalar inequalities related to the nonzero eigenvalues of the Laplacian, which requires the diagonalizability of the Laplacian, while the second method characterizes the parameters by linear matrix inequalities, which circumvents the requirement of the diagonalizability of the Laplacian. Compared with existing results, the proposed approach can simultaneously cope with heterogeneous uncertainties and directed communication graphs. Numerical results verify the advantages of the proposed design methods.
       
  • Event-triggered identification of FIR systems with binary-valued output
           observations
    • Abstract: Publication date: December 2018Source: Automatica, Volume 98Author(s): Jing-Dong Diao, Jin Guo, Chang-Yin Sun This paper investigates the identification of FIR (finite impulse response) systems whose output observations are subject to both the binary-valued quantization and the event-triggered scheme. Based on the a priori information of the unknown parameters and the statistical property of the system noise, a recursive stochastic-approximation-type identification algorithm is proposed. Under a class of persistently exciting inputs, the algorithm is proved to be strongly convergent and the convergence rate of the estimation error is also established, where the corresponding event-triggering conditions are provided. Moreover, the communication rate is discussed. A numerical example is included to verify the effectiveness of the results obtained.
       
  • Modeling and iterative pulse-shape control of optical chirped pulse
           amplifiers
    • Abstract: Publication date: December 2018Source: Automatica, Volume 98Author(s): Andreas Deutschmann, Pavel Malevich, Andrius Baltuška, Andreas Kugi In this paper, we present an iterative learning algorithm for pulse-shape control applications of optical chirped pulse amplifiers for ultra-short, high-energy light pulses. For this, we first introduce a general nonlinear and infinite-dimensional mathematical model of chirped pulse amplifiers. By reducing the complexity of this detailed model and reformulating the control task, we are subsequently able to apply inversion-based iterative learning control to track desired output pulses. Using the reduced model to estimate both internal states and unknown parameters yields a fast and simple way of consistently estimating the input–output behavior without relying on a calibrated system model. The effectiveness of the resulting adaptive algorithm is finally illustrated with simulation scenarios on an experimentally validated mathematical model.
       
  • Combinatorial methods for invariance and safety of hybrid systems
    • Abstract: Publication date: December 2018Source: Automatica, Volume 98Author(s): Nikolaos Athanasopoulos, Raphaël M. Jungers Inspired by Switching Systems and Automata theory, we investigate how combinatorial analysis techniques can be performed on a hybrid automaton in order to enhance its safety or invariance analysis. We focus on the particular case of Constrained Switching Systems, that is, hybrid automata with linear dynamics and no guards. We follow two opposite approaches, each with unique benefits: First, we construct invariant sets via the ‘Reduced’ system, induced by a smaller graph which consists of the essential nodes, called the unavoidable nodes. The computational amelioration of working with a smaller, and in certain cases the minimum necessary number of nodes, is significant. Second, we exploit graph liftings, in particular the Iterated Dynamics Lift (T-Lift) and the Path-Dependent Lift (P-Lift). For the former case, we show that invariant sets can be computed in a fraction of the iterations compared to the non-lifted case, while we show how the latter can be utilized to compute non-convex approximations of invariant sets of a controlled complexity.We also revisit well studied problems, highlighting the potential benefits of the approach. In particular, we apply our framework to (i) invariant sets computations for systems with dwell-time restrictions, (ii) fast computations of the maximal invariant set for uncertain linear systems and (iii) non-convex approximations of the minimal invariant set for arbitrary switching linear systems.
       
  • Exponential convergence under distributed averaging integral frequency
           control
    • Abstract: Publication date: December 2018Source: Automatica, Volume 98Author(s): Erik Weitenberg, Claudio De Persis, Nima Monshizadeh We investigate the performance and robustness of distributed averaging integral controllers used in the optimal frequency regulation of power networks. We construct a strict Lyapunov function that allows us to quantify the exponential convergence rate of the closed-loop system. As an application, we study the stability of the system in the presence of disruptions to the controllers’ communication network, and investigate how the convergence rate is affected by these disruptions.
       
  • Control for networked control systems with remote and local controllers
           over unreliable communication channel
    • Abstract: Publication date: December 2018Source: Automatica, Volume 98Author(s): Xiao Liang, Juanjuan Xu This paper is concerned with the problems of optimal control and stabilization for networked control systems (NCSs), where the remote controller and the local controller operate the linear plant simultaneously. The main contributions are two-fold. Firstly, a necessary and sufficient condition for the finite horizon optimal control problem is given in terms of the two Riccati equations. Secondly, it is shown that the system without the additive noise is stabilizable in the mean square sense if and only if the two algebraic Riccati equations admit the unique solutions, and a sufficient condition is given for the boundedness in the mean square sense of the system with the additive noise. Numerical examples about unmanned aerial vehicles model are shown to illustrate the effectiveness of the proposed algorithm.
       
  • Suboptimal receding horizon estimation via noise blocking
    • Abstract: Publication date: December 2018Source: Automatica, Volume 98Author(s): He Kong, Salah Sukkarieh For discrete-time linear systems, we propose a suboptimal approach to constrained estimation so that the associated computation burden is reduced. This is achieved by enforcing a move blocking (MB) structure in the estimated process noise sequence (PNS). We show that full information estimation (FIE) and receding horizon estimation (RHE) with MB are both stable in the sense of an observer. The techniques in proving stability are inspired by those that have been proposed for standard RHE. To be specific, stability results are mainly achieved by (i) carefully embellishing the general assumptions for standard RHE to accommodate the MB requirement; (ii) exploiting the principle of optimality, as well as convexity of the quadratic programs (QPs) associated with FIE and RHE; (iii) relying on the fact that the Kalman filter is the best linear estimator in the least-squares sense. A crucial requirement in achieving stability for MB RHE is that the segment structure (SS) of the PNS of MB FIE for the optimization steps within the receding horizon (i.e., steps between T−N and T−1) has to be enforced in the MB RHE optimization. As a result, the MB RHE strategy becomes a dynamic estimator with a periodically varying computational complexity. The theoretical results have been illustrated with examples.
       
  • Sliding mode observers for a network of thermal and hydroelectric power
           plants
    • Abstract: Publication date: December 2018Source: Automatica, Volume 98Author(s): Gianmario Rinaldi, Michele Cucuzzella, Antonella Ferrara This paper deals with the design of a novel sliding mode observer-based scheme to estimate and reconstruct the unmeasured state variables in power networks including hydroelectric power plants and thermal power plants. The proposed approach reveals to be flexible to topological changes to power networks and can be easily updated only where changes occur. The discussed numerical simulations validate the effectiveness of the proposed estimation scheme.
       
  • Robust adaptive fault tolerant control for a linear cascaded ODE-beam
           system
    • Abstract: Publication date: December 2018Source: Automatica, Volume 98Author(s): Zhijie Liu, Jinkun Liu, Wei He In this paper, we present fault tolerant control design for a class of cascaded systems described by ordinary differential equations (ODEs) and an Euler–Bernoulli beam (EBB). The objective of this study is to design a robust adaptive fault tolerant control such that the global stability of the resulting closed-loop cascaded system is ensured and asymptotic tracking can be achieved subject to actuator failures, parameter uncertainty and external disturbances. The Lyapunov’s direct method is used to design the control schemes and prove the stability of the closed-loop system. Finally, the results are illustrated using numerical simulations for control performance verification.
       
  • Fault tolerant control for a class of interconnected asynchronous
           sequential machines
    • Abstract: Publication date: December 2018Source: Automatica, Volume 98Author(s): Jung-Min Yang This paper considers fault tolerant control for parallel interconnected asynchronous sequential machines (ASMs) governed by a single corrective controller with output feedback. The control objective is to diagnose unauthorized state transitions and to recover the normal input/output behavior of the closed-loop system in an asynchronous mechanism. The existence condition and design algorithm for a fault tolerant controller is addressed in the framework of corrective control. The proposed scheme is efficient in that it does not require complete modeling of parallel composition nor output bursts in the feedback channel. An illustrative example is provided to demonstrate the procedure of controller synthesis.
       
  • Two-stage information filters for single and multiple sensors, and their
           square-root versions
    • Abstract: Publication date: December 2018Source: Automatica, Volume 98Author(s): Kumar Pakki Bharani Chandra, Mohamed Darouach Accurate states and unknown random bias estimation for well- and ill-conditioned systems are crucial for several applications. In this paper, a fusion of a two-stage Kalman filter and an information filter, and its extensions are considered to estimate the state variables and unknown random bias. Specifically, we propose four extensions of two-stage Kalman filters: two-stage information filter (TSIF), multi-sensor two-stage information filter (M-TSIF) and their square-root versions. The TSIF deals with single-sensor systems whereas the M-TSIF is capable to handle multi-sensor systems. For ill-conditioned systems, numerically stable square-root versions of TSIF and M-TSIF are developed. The performance of the proposed filters (along with the existing two-stage Kalman filter), for well- and ill-conditioned cases, is demonstrated on a quadruple-tank model.
       
  • Analysis of Lur’e dominant systems in the frequency domain
    • Abstract: Publication date: December 2018Source: Automatica, Volume 98Author(s): Félix A. Miranda-Villatoro, Fulvio Forni, Rodolphe J. Sepulchre Frequency domain analysis of linear time-invariant (LTI) systems in feedback with static nonlinearities is a classical and fruitful topic of nonlinear systems theory. We generalize this approach beyond equilibrium stability analysis with the aim of characterizing feedback systems whose asymptotic behavior is low dimensional. We illustrate the theory with a generalization of the circle criterion for the analysis of multistable and oscillatory Lur’e feedback systems.
       
  • Optimal multirate sampling in symbolic models for incrementally stable
           switched systems
    • Abstract: Publication date: December 2018Source: Automatica, Volume 98Author(s): Adnane Saoud, Antoine Girard Methods for computing approximately bisimilar symbolic models for incrementally stable switched systems are often based on discretization of time and space, where the value of time and space sampling parameters must be carefully chosen in order to achieve a desired precision. These approaches can result in symbolic models that have a very large number of transitions, especially when the time sampling, and thus the space sampling parameters are small. In this paper, we present an approach to the computation of symbolic models for switched systems with dwell-time constraints using multirate time sampling, where the period of symbolic transitions is a multiple of the control (i.e. switching) period. We show that all the multirate symbolic models, resulting from the proposed construction, are approximately bisimilar to the original incrementally stable switched system with the precision depending on the sampling parameters, and the sampling factor between transition and control periods. The main contribution of the paper is the explicit determination of the optimal sampling factor, which minimizes the number of transitions in the class of proposed symbolic models for a prescribed precision. Interestingly, we prove that this optimal sampling factor is mainly determined by the state space dimension and the number of modes of the switched system. Finally, an illustration of the proposed approach is shown on an example, which shows the benefit of multirate symbolic models in reducing the computational cost of abstraction-based controller synthesis.
       
  • Boundary observability of wave equations with nonlinear van der Pol type
           boundary conditions
    • Abstract: Publication date: Available online 21 September 2018Source: AutomaticaAuthor(s): Shuting Cai, Mingqing Xiao In this note we study the boundary observability for one-dimensional wave equation associated with nonlinear boundary condition that can generate complex dynamics. We discuss the exact observability and approximate observability, respectively, in terms of three different types of common boundary observations by studying the wave interactions on the boundary directly.
       
  • Analysis of averages over distributions of Markov processes
    • Abstract: Publication date: Available online 21 September 2018Source: AutomaticaAuthor(s): Patricio E. Valenzuela, Cristian R. Rojas, Håkan Hjalmarsson In problems of optimal control of Markov decision processes and optimal design of experiments, the occupation measure of a Markov process is designed in order to maximize a specific reward function. When the memory of such a process is too long, or the process is non-Markovian but mixing, it makes sense to approximate it by that of a shorter memory Markov process. This note provides a specific bound for the approximation error introduced in these schemes. The derived bound is then applied to the proposed solution of a recently introduced approach to optimal input design for nonlinear systems.
       
  • Linear programming based time lag identification in event sequences
    • Abstract: Publication date: December 2018Source: Automatica, Volume 98Author(s): Marco F. Huber, Marc-André Zöller, Marcus Baum Many technical systems like manufacturing plants or software applications generate large event sequences. Knowing the temporal relationship between events is important for gaining insights into the status and behavior of the system. This paper proposes a novel approach for identifying the time lag between different event types. This identification task is formulated as a binary integer optimization problem that can be solved efficiently and close to optimality by means of a linear programming approximation. The performance of the proposed approach is demonstrated on synthetic and real-world event sequences.
       
  • Particle Gaussian mixture filters-I
    • Abstract: Publication date: Available online 14 September 2018Source: AutomaticaAuthor(s): Dilshad Raihan, Suman Chakravorty In this paper, we propose a particle based Gaussian mixture filtering approach for nonlinear estimation that is free of the particle depletion problem inherent to most particle filters. We employ an ensemble of possible state realizations for the propagation of state probability density. A Gaussian mixture model (GMM) of the propagated uncertainty is then recovered by clustering the ensemble. The posterior density is obtained subsequently through a Kalman measurement update of the mixture modes. We prove the convergence in probability of the resultant density to the true filter density assuming exponential forgetting of initial conditions. The performance of the proposed filtering approach is demonstrated through several test cases and is extensively compared to other nonlinear filters.
       
  • A distributed approach to robust control of multi-robot systems
    • Abstract: Publication date: December 2018Source: Automatica, Volume 98Author(s): Yuan Zhou, Hesuan Hu, Yang Liu, Shang-Wei Lin, Zuohua Ding Motion planning of multi-robot systems has been extensively investigated. Many proposed approaches assume that all robots are reliable. However, robots with priori known levels of reliability may be used in applications to account for: (1) the cost in terms of unit price per robot type, and (2) the cost in terms of robot wear in long term deployment. In the former case, higher reliability comes at a higher price, while in the latter replacement may cost more than periodic repairs, e.g., buses, trams, and subways. In this study, we investigate robust control of multi-robot systems, such that the number of robots affected by the failed ones is minimized. It should mandate that the failure of a robot can only affect the motion of robots that collide directly with the failed one. We assume that the robots in a system are divided into reliable and unreliable ones, and each robot has a predetermined and closed path to execute persistent tasks. By modeling each robot’s motion as a labeled transition system, we propose two distributed robust control algorithms: one for reliable robots and the other for unreliable ones. The algorithms guarantee that wherever an unreliable robot fails, only the robots whose state spaces contain the failed state are blocked. Theoretical analysis shows that the proposed algorithms are practically operative. Simulations with seven robots are carried out and the results show the effectiveness of our algorithms.
       
  • Particle Gaussian mixture filters-II
    • Abstract: Publication date: Available online 13 September 2018Source: AutomaticaAuthor(s): Dilshad Raihan, Suman Chakravorty In our previous work, we proposed a particle Gaussian mixture (PGM-I) filter for nonlinear estimation. The PGM-I filter uses the transition kernel of the state Markov chain to sample from the propagated prior. It constructs a Gaussian mixture representation of the propagated prior density by clustering the samples. The measurement data are incorporated by updating individual mixture modes using the Kalman measurement update. However, the Kalman measurement update is inexact when the measurement function is nonlinear and leads to the restrictive assumption that the number of modes remains fixed during the measurement update. In this paper, we introduce an alternate PGM-II filter that employs parallelized Markov Chain Monte Carlo (MCMC) sampling to perform the measurement update. The PGM-II filter update is asymptotically exact and does not enforce any assumptions on the number of Gaussian modes. The PGM-II filter is employed in the estimation of two test case systems. The results indicate that the PGM-II filter is suitable for handling nonlinear/non-Gaussian measurement update.
       
 
 
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