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IEEE Transactions on Automatic Control
Journal Prestige (SJR): 3.433
Citation Impact (citeScore): 6
Number of Followers: 66  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0018-9286
Published by IEEE Homepage  [228 journals]
  • IEEE Control Systems Society

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      Abstract: Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • IEEE Control Systems Society

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      Abstract: These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • Nonpathological ISS-Lyapunov Functions for Interconnected Differential
           Inclusions

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      Authors: Matteo Della Rossa;Aneel Tanwani;Luca Zaccarian;
      Pages: 3774 - 3789
      Abstract: This article concerns robustness analysis for interconnections of two dynamical systems (described by upper semicontinuous differential inclusions) using a generalized notion of derivatives associated with locally Lipschitz Lyapunov functions obtained from a finite family of differentiable functions. We first provide sufficient conditions for input-to-state stability for differential inclusions, using a class of nonsmooth (but locally Lipschitz) candidate Lyapunov functions and the concept of Lie generalized derivative. In general our conditions are less conservative than the more common Clarke derivative-based conditions. We apply our result to state-dependent switched systems, and to the interconnection of two differential inclusions. As an example, we propose an observer-based controller for certain nonlinear two-mode state-dependent switched systems.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • An Explicit Parametrization of Closed Loops for Spatially Distributed
           Controllers With Sparsity Constraints

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      Authors: Emily Jensen;Bassam Bamieh;
      Pages: 3790 - 3805
      Abstract: In this article,we study the linear time-invariant state-feedback controller design problem for distributed systems. We follow the recently developed system level synthesis (SLS) approach and impose locality structure on the resulting closed-loop mappings; the corresponding controller implementation inherits this prescribed structure. In contrast to existing SLS results, we derive an explicit (rather than implicit) parameterization of all achievable stabilized closed-loops. This admits more efficient IIR representations of the temporal part of the closed-loop dynamics, and it allows for the $mathcal {H}_2$ design problem with closed-loop spatial sparsity constraints to be converted to a standard model matching problem, with the number of transfer function parameters scaling linearly with the closed-loop spatial extent constraint. We illustrate our results with two applications: consensus of first-order subsystems and the vehicular platoons problem. In the case of first-order consensus, we provide analytic solutions and further analyze the architecture of the resulting controller implementation. Results for infinite extent spatially invariant systems are presented to provide insight to the case of a large but finite number of subsystems.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • Linear–Quadratic Optimal Control for Discrete-Time Mean-Field
           Systems With Input Delay

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      Authors: Qingyuan Qi;Lihua Xie;Huanshui Zhang;
      Pages: 3806 - 3821
      Abstract: The linear–quadratic (LQ) optimal control and stabilization problems for mean-field systems with input delay (MFSID) are investigated in this article. The necessary and sufficient solvability conditions for LQ control of MFSID are first given in terms of a convexity condition and the solvability of equilibrium conditions. Consequently, by solving the associated mean-field forward and backward stochastic difference equations, the optimal control is derived in terms of the solution of a modified Riccati equation. Furthermore, for the infinite-horizon case, the stabilization problem for MFSID is studied, and the necessary and sufficient stabilizability conditions are obtained. We show that MFSID can be mean square stabilizable if and only if a modified algebraic Riccati equation admits a unique positive-definite solution.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • Sequential Source Coding for Stochastic Systems Subject to Finite Rate
           Constraints

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      Authors: Photios A. Stavrou;Mikael Skoglund;Takashi Tanaka;
      Pages: 3822 - 3835
      Abstract: In this article, we revisit the sequential source-coding framework to analyze fundamental performance limitations of discrete-time stochastic control systems subject to feedback data-rate constraints in finite-time horizon. The basis of our results is a new characterization of the lower bound on the minimum total-rate achieved by sequential codes subject to a total (across time) distortion constraint and a computational algorithm that allocates optimally the rate-distortion, for a given distortion level, at each instant of time and any fixed finite-time horizon. The idea behind this characterization facilitates the derivation of analytical, nonasymptotic, and finite-dimensional lower and upper bounds in two control-related scenarios: a) A parallel time-varying Gauss–Markov process with identically distributed spatial components that are quantized and transmitted through a noiseless channel to a minimum mean-squared error decoder; and b) a time-varying quantized linear quadratic Gaussian (LQG) closed-loop control system, with identically distributed spatial components and with a random data-rate allocation. Our nonasymptotic lower bound on the quantized LQG control problem reveals the absolute minimum data-rates for (mean square) stability of our time-varying plant for any fixed finite-time horizon. We supplement our framework with illustrative simulation experiments.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • Nonlinear Dynamic Systems Parameterization Using Interval-Based Global
           Optimization: Computing Lipschitz Constants and Beyond

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      Authors: Sebastian A. Nugroho;Ahmad F. Taha;Vu Hoang;
      Pages: 3836 - 3850
      Abstract: Numerous state-feedback and observer designs for nonlinear dynamic systems (NDS) have been developed in the past three decades. These designs assume that NDS nonlinearities satisfy one of the following function set classifications: bounded Jacobian, Lipschitz continuity, one-sided Lipschitz, quadratic inner-boundedness, and quadratic boundedness. These function sets are characterized by constant scalars or matrices bounding the NDS’ nonlinearities. These constants depend on the NDS’ operating region, topology, and parameters and are utilized to synthesize observer/controller gains. Unfortunately, there is a near-complete absence of algorithms to compute such bounding constants. In this article, we develop analytical and computational methods to compute such constants. First, for every function set classification, we derive analytical expressions for these bounding constants through global maximization formulations. Second, we utilize a derivative-free interval-based global maximization algorithm based on the branch-and-bound framework to numerically obtain the bounding constants. Third, we showcase the effectiveness of our approaches to compute the corresponding parameters on some NDS such as highway traffic networks and synchronous generator models.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • Barrier Function-Based Adaptive Lyapunov Redesign for Systems Without A
           Priori Bounded Perturbations

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      Authors: Christopher D. Cruz-Ancona;Manuel A. Estrada;Leonid Fridman;
      Pages: 3851 - 3862
      Abstract: The problem of an adaptive Lyapunov redesign is revisited for a class of systems without a priori knowledge of the function majoring nonlinear uncertainties and disturbances. An adaptive barrier function-based gain for unit control is proposed, ensuring an arbitrary a priori predefined uniform ultimate bound for solutions despite the presence of uncertainties and disturbances. The usage of positive semi-definite barrier function generates a continuous control signal adjusting the chattering, when the perturbations are decreasing to zero.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • Mean-Field Game for Collective Decision-Making in Honeybees via Switched
           Systems

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      Authors: Leonardo Stella;Dario Bauso;Patrizio Colaneri;
      Pages: 3863 - 3878
      Abstract: In this article, we study the optimal control problem arising from the mean-field game formulation of the collective decision-making in honeybee swarms. A population of homogeneous players (the honeybees) has to reach consensus on one of two options. We consider three states: the first two represent the available options (or strategies), and the third one represents the uncommitted state. We formulate the continuous-time discrete-state mean-field game model. The contributions of this article are the following: 1) we propose an optimal control model where players have to control their transition rates to minimize a running cost and a terminal cost, in the presence of an adversarial disturbance; 2) we develop a formulation of the micro–macro model in the form of an initial-terminal value problem with switched dynamics; 3) we study the existence of stationary solutions and the mean-field Nash equilibrium for the resulting switched system; 4) we show that under certain assumptions on the parameters, the game may admit periodic solutions; and 5) we analyze the resulting microscopic dynamics in a structured environment where a finite number of players interact through a network topology.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • Resilient Control Under Quantization and Denial-of-Service: Codesigning a
           Deadbeat Controller and Transmission Protocol

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      Authors: Wenjie Liu;Jian Sun;Gang Wang;Francesco Bullo;Jie Chen;
      Pages: 3879 - 3891
      Abstract: This article is concerned with the problem of stabilizing continuous-time linear time-invariant (LTI) systems subject to quantization and denial-of-service (DoS) attacks. In this context, two DoS-induced challenges emerge in the design of resilient encoding schemes, namely, the coupling between encoding strategies of different signals, and the synchronization between the encoder and decoder. These challenges are addressed by a novel proposed structure based on a deadbeat controller as well as a delicate transmission protocol for the input and output channels, and codesigned leveraging the controllability index. When both input and output channels are subject to DoS attacks and quantization, the proposed structure is shown able to decouple the encoding schemes for input, output, and estimated output signals. This property is further corroborated by designing encoding schemes as well as conditions ensuring exponential stability of the closed-loop system. On the other hand, when only the output channel is subject to network attack phenomena, the proposed structure can achieve exponential stabilization without acknowledgment (ACK) signals, in contrast to existing ACK-based results. Finally, a numerical example is given to demonstrate the practical merits of the proposed theoretical and practical approach.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • Unfalsified Adaptive Control for Nonlinear Time-Varying Plants

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      Authors: Sagar V. Patil;Yu-Chen Sung;Michael G. Safonov;
      Pages: 3892 - 3904
      Abstract: The Battistelli–Hespanha–Mosca–Tesi (BHMT) adaptive reset switching algorithm is enhanced to admit bumpless transfer and to control a nonlinear time-varying plant using the broad class of two-degree-of-freedom nonlinear time-varying controllers. Performance, including stability and exponential stability, is proved given only that the adaptive stabilization problem is $lambda$-feasible, which means at every time, there exists at least one exponentially stabilizing controller. We relax two restrictive BHMT assumptions: i) the plant is linear time-invariant (LTI) over most intervals and has finite order and ii) each controller is LTI. And, we expand the scope to include a broad class of bumpless switching algorithms. An example is provided to support the improved results.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • Stochastic Generalized Nash Equilibrium-Seeking in Merely Monotone Games

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      Authors: Barbara Franci;Sergio Grammatico;
      Pages: 3905 - 3919
      Abstract: We solve the stochastic generalized Nash equilibrium (SGNE) problem in merely monotone games with expected value cost functions. Specifically, we present the first distributed SGNE-seeking algorithm for monotone games that require one proximal computation (e.g., one projection step) and one pseudogradient evaluation per iteration. Our main contribution is to extend the relaxed forward–backward operator splitting by the Malitsky (Mathematical Programming, 2019) to the stochastic case and in turn to show almost sure convergence to an SGNE when the expected value of the pseudogradient is approximated by the average over a number of random samples.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • Decentralized Control of Multiagent Systems Using Local Density Feedback

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      Authors: Shiba Biswal;Karthik Elamvazhuthi;Spring Berman;
      Pages: 3920 - 3932
      Abstract: In this article, we stabilize a discrete-time Markov process evolving on a compact subset of $mathbb {R}^d$ to an arbitrary target distribution that has an $L^infty (cdot)$ density and does not necessarily have a connected support on the state space. We address this problem by stabilizing the corresponding Kolmogorov forward equation, the mean-field model of the system, using a density-dependent transition Kernel as the control parameter. Our main application of interest is controlling the distribution of a multiagent system in which each agent evolves according to this discrete-time Markov process. To prevent agent state transitions at the equilibrium distribution, which would potentially waste energy, we show that the Markov process can be constructed in such a way that the operator that pushes forward measures is the identity at the target distribution. In order to achieve this, the transition kernel is defined as a function of the current agent distribution, resulting in a nonlinear Markov process. Moreover, we design the transition kernel to be decentralized in the sense that it depends only on the local density measured by each agent. We prove the existence of such a decentralized control law that globally stabilizes the target distribution. Furthermore, to implement our control approach on a finite $N$-agent system, we smoothen the mean-field dynamics via the process of mollification. We validate our control law with numerical simulations of multiagent systems with different population sizes. We observe that as $N$ increases, the agent distribution in the $N$-agent simulations converges to the solution of the mean-field model, and the number of agent state transitions at equilibrium decreases to zero.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • Codiagnosability of Networked Discrete Event Systems With Timing Structure

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      Authors: Gustavo S. Viana;Marcos V. S. Alves;João Carlos Basilio;
      Pages: 3933 - 3948
      Abstract: We address, in this article, the problem of codiagnosability of networked discrete event systems with timing structure (NDESWTS) subject to delays and loss of observations of events between the measurement sites (MS) and local diagnosers (LD). To this end, we first introduce a new timed model that represents the dynamic behavior of the plant based on the, a priori, knowledge of the minimal activation time for each transition of the plant and on the maximal delays in the communication channels that connect MS and LD. We then convert this timed model into an equivalent untimed one, and add possible intermittent packet loss in the communication network. Based on this untimed model, we present necessary and sufficient conditions for NDESWTS codiagnosability and propose two tests for its verification: one that deploys diagnosers and another one that uses verifiers.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • Distributed Gradient Flow: Nonsmoothness, Nonconvexity, and Saddle Point
           Evasion

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      Authors: Brian Swenson;Ryan Murray;H. Vincent Poor;Soummya Kar;
      Pages: 3949 - 3964
      Abstract: The article considers distributed gradient flow (DGF) for multiagent nonconvex optimization. DGF is a continuous-time approximation of distributed gradient descent that is often easier to study than its discrete-time counterpart. The article has two main contributions. First, the article considers optimization of nonsmooth, nonconvex objective functions. It is shown that DGF converges to critical points in this setting. The article then considers the problem of avoiding saddle points. It is shown that if agents’ objective functions are assumed to be smooth and nonconvex, then DGF can only converge to a saddle point from a zero-measure set of initial conditions. To establish this result, the article proves a stable manifold theorem for DGF, which is a fundamental contribution of independent interest. In a companion article, analogous results are derived for discrete-time algorithms.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • Multiagent Interval Consensus With Flocking Dynamics

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      Authors: Jiahu Qin;Qichao Ma;Peng Yi;Long Wang;
      Pages: 3965 - 3980
      Abstract: In this article, we investigate the interval consensus for a network of agents with flocking dynamics, i.e., second-order multiagent systems, where each agent imposes an interval constraint on its preferred consensus values, with the aim of driving the agent into a favorable interval. Specifically, we work on two different frameworks of interval constraints, viz., the first one that the node states are constrained in their own constraint intervals and the second one that the node states are constrained in their neighbors’ constraint intervals. For both of the frameworks, we provide a complete solution to the equilibrium seeking problem by resolving a system of nonlinear equations. It is proved that if the underlying graph is strongly connected and the intersection of constraint intervals is empty, then there exists a unique equilibrium point; and if the intersection is nonempty, then there exist multiple equilibrium points, all of which lead to state consensus. We also establish several conditions for the local stability of the unique equilibrium point (corresponding to the case with empty intersection of constraint intervals) or local constraint consensus (corresponding to the case with nonempty intersection of constraint intervals) by invoking Lyapunov’s indirect method. Characterization of the global behavior of such a second-order multiagent system is technically rather challenging at this stage. As a first step toward this end, we show in two special cases that global convergence to the unique equilibrium point or state consensus can be guaranteed by employing Lyapunov stability theory and robust analysis techniques. Finally, some numerical examples are provided to illustrate the theoretical findings.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • On the Analysis of Inexact Augmented Lagrangian Schemes for Misspecified
           Conic Convex Programs

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      Authors: Necdet Serhat Aybat;Hesam Ahmadi;Uday V. Shanbhag;
      Pages: 3981 - 3996
      Abstract: In this article, we consider the misspecified optimization problem of minimizing a convex function $f(x;theta ^*)$ in $x$ over a conic constraint set represented by $h(x;theta ^*) in mathcal {K}$, where $theta ^*$ is an unknown (or misspecified) vector of parameters, $mathcal {K}$ is a closed convex cone, and $h$ is affine in $x$. Suppose that $theta ^*$ is unavailable but may be learnt by a separate process that generates a sequence of estimators $theta _k$, each of which is an increasingly accurate approximation of $theta ^*$. We develop a first-order inexact augmented Lagrangian (AL) scheme for computing an optimal solution $x^*$ corresponding to $theta ^*$ while simultaneously learning $theta ^*$. In particular, we derive rate statements for such schemes when the penalty parameter sequence is either constant or increasing and derive bounds on the overall complexity in terms of proximal gradient steps when AL subproblems are inexactly solved via an accelerated proximal gradient scheme. Numerical results for a portfolio opt-mization problem with a misspecified covariance matrix suggest that these schemes perform well in practice, while naive sequential schemes may perform poorly in comparison.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • Multiagent Persistent Monitoring of Targets With Uncertain States

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      Authors: Samuel C. Pinto;Sean B. Andersson;Julien M. Hendrickx;Christos G. Cassandras;
      Pages: 3997 - 4012
      Abstract: We address the problem of persistent monitoring, where a finite set of mobile agents has to persistently visit a finite set of targets. Each of these targets has an internal state that evolves with linear stochastic dynamics. The agents can observe these states, and the observation quality is a function of the distance between the agent and a given target. The goal is then to minimize the mean squared estimation error of these target states. We approach the problem from an infinite horizon perspective, where we prove that, under some natural assumptions, the covariance matrix of each target converges to a limit cycle. The goal, therefore, becomes to minimize the steady-state uncertainty. Assuming that the trajectory is parameterized, we provide tools for computing the steady-state cost gradient. We show that, in 1-D (one dimensional) environments with bounded control and nonoverlapping targets, when an optimal control exists it can be represented using a finite number of parameters. We also propose an efficient parameterization of the agent trajectories for multidimensional settings using Fourier curves. Simulation results show the efficacy of the proposed technique in 1-D, 2-D, and 3-D scenarios.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • Necessary and Sufficient Conditions for Harmonic Control in Continuous
           Time

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      Authors: Nicolas Blin;Pierre Riedinger;Jamal Daafouz;Louis Grimaud;Philippe Feyel;
      Pages: 4013 - 4028
      Abstract: In this article, we revisit the concepts and tools of harmonic analysis and control and provide a rigorous mathematical answer to the following question: When does an harmonic control have a representative in the time domain' By representative we mean a control in the time domain that leads by sliding Fourier decomposition to exactly the same harmonic control. Harmonic controls that do not have such representatives lead to erroneous results in practice. The main results of this article are: A one-to-one correspondence between ad hoc functional spaces guaranteeing the existence of a representative, a strict equivalence between the Caratheorody solutions of a differential system and the solutions of the associated harmonic differential model, and as a consequence, a general harmonic framework for linear time periodic systems and bilinear affine systems. The proposed framework allows to design globally stabilizing harmonic control laws. We illustrate the proposed approach on a single-phase rectifier bridge. Through this example, we show how one can design stabilizing control laws that guarantee periodic disturbance rejection and low harmonic content.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • Bounded Estimation Over Finite-State Channels: Relating Topological
           Entropy and Zero-Error Capacity

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      Authors: Amir Saberi;Farhad Farokhi;Girish N. Nair;
      Pages: 4029 - 4044
      Abstract: We investigate state estimation of linear systems over channels having a finite state not known by the transmitter or receiver. We show that similar to memoryless channels, zero-error capacity is the right figure of merit for achieving bounded estimation errors. We then consider finite-state, worst-case versions of the common erasure, and additive noise channels models, in which the noise is governed by a finite-state machine without any statistical structure. Upper and lower bounds on their zero-error capacities are derived, revealing a connection with the topological entropy of the channel dynamics. Separate necessary and sufficient conditions for bounded linear state estimation errors via such channels are obtained. These estimation conditions bring together the topological entropies of the linear system and the discrete channel.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • Reduced-Order Nonlinear Observers Via Contraction Analysis and Convex
           Optimization

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      Authors: Bowen Yi;Ruigang Wang;Ian R. Manchester;
      Pages: 4045 - 4060
      Abstract: In this article, we propose a new approach to design globally convergent reduced-order observers for nonlinear control systems via contraction analysis and convex optimization. Despite the fact that contraction is a concept naturally suitable for state estimation, the existing solutions are either local or relatively conservative when applying to physical systems. To address this, we show that this problem can be translated into an offline search for a coordinate transformation after which the dynamics is (transversely) contracting. The obtained sufficient condition consists of some easily verifiable differential inequalities, which, on one hand, identify a very general class of “detectable” nonlinear systems, and on the other hand, can be expressed as computationally efficient convex optimization, making the design procedure more systematic. Connections with some well-established approaches and concepts are also clarified in this article. Finally, we illustrate the proposed method with several numerical and physical examples, including polynomial, mechanical, electromechanical, and biochemical systems.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • A Second Order Primal-Dual Method for Nonsmooth Convex Composite
           Optimization

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      Authors: Neil K. Dhingra;Sei Zhen Khong;Mihailo R. Jovanović;
      Pages: 4061 - 4076
      Abstract: We develop a second order primal-dual method for optimization problems in which the objective function is given by the sum of a strongly convex twice differentiable term and a possibly nondifferentiable convex regularizer. After introducing an auxiliary variable, we utilize the proximal operator of the nonsmooth regularizer to transform the associated augmented Lagrangian into a function that is once, but not twice, continuously differentiable. The saddle point of this function corresponds to the solution of the original optimization problem. We employ a generalization of the Hessian to define second-order updates on this function and prove global exponential stability of the corresponding differential inclusion. Furthermore, we develop a globally convergent customized algorithm that utilizes the primal-dual augmented Lagrangian as a merit function. We show that the search direction can be computed efficiently and prove quadratic/superlinear asymptotic convergence. We use the $ell _1$-regularized model predictive control problem and the problem of designing a distributed controller for a spatially invariant system to demonstrate the merits and the effectiveness of our method.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • Event-Triggered Control for Switched Systems With Denial-of-Service Attack

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      Authors: Rui Zhao;Zhiqiang Zuo;Yijing Wang;
      Pages: 4077 - 4090
      Abstract: This article studies the stabilization problem for switched systems in the presence of denial-of-service (DoS) attack using the event-triggered scheme. Unlike the traditional switching signal design with DoS attack, some challenges arise in the joint influence on the constraints of DoS duration/frequency, and the asynchronous behavior caused by the controller and the subsystem mode. To this end, a novel multiple Lyapunov function involving the joint effects of DoS attack and controller mode is constructed. This allows that there are no events between two consecutive switching instants. By incorporating the dwell-time switching signal with the event-triggered scheme, the considered system can be globally exponentially stabilizable when the frequency and duration of DoS attack meet certain requirements. It also shows that apart from the codesign associated with the event-triggered parameters with controller gain, the maximum allowable sampling period can be offline calculated. Moreover, the Zeno behavior is eliminated by calculating a lower bound of the event-triggered interval, over which the DoS attack is inactive. Finally, simulations are carried out to verify the effectiveness of the theoretical results.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • Guiding Vector Fields for Following Occluded Paths

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      Authors: Weijia Yao;Bohuan Lin;Brian D. O. Anderson;Ming Cao;
      Pages: 4091 - 4106
      Abstract: Accurately following a geometric desired path in a two-dimensional (2-D) space is a fundamental task for many engineering systems, in particular mobile robots. When the desired path is occluded by obstacles, it is necessary and crucial to temporarily deviate from the path for obstacle/collision avoidance. In this article, we develop a composite guiding vector field via the use of smooth bump functions and provide theoretical guarantees that the integral curves of the vector field can follow an arbitrary sufficiently smooth desired path and avoid collision with obstacles of arbitrary shapes. These two behaviors are reactive since path (re)planning and global map construction are not involved. To deal with the common deadlock problem, we introduce a switching vector field, and the Zeno behavior is excluded. Simulations are conducted to support the theoretical results.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • Asynchronous Output Feedback Control of Hidden Semi-Markov Jump Systems
           With Random Mode-Dependent Delays

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      Authors: Yongxiao Tian;Huaicheng Yan;Hao Zhang;Jun Cheng;Hao Shen;
      Pages: 4107 - 4114
      Abstract: This article is concerned with the problem of output feedback control for a class of continuous-time hidden semi-Markov jump systems with time delays. Due to the limitations of the actual environment, system modes are usually undetectable, which are called hidden modes. The controller modes are described as observable modes. Emission probabilities are used to establish the relationship between abovementioned two concepts. The jump parameters are governed by the hidden semi-Markov process, which can better describe the asynchronous information between the controller modes and the system modes. Besides, time delays are considered to be time-varying and dependent on the hidden modes. By employing some mathematical transformation and constructing a novel Lyapunov–Krasovskii functional, some new parameter-dependent sufficient stabilization conditions can be obtained by designing an observed-mode-dependent static output-feedback controller. Finally, a practical example is provided to illustrate the effectiveness and merits of the proposed methods.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • Structured Preconditioning of Conjugate Gradients for Path-Graph Network
           Optimal Control Problems

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      Authors: Armaghan Zafar;Michael Cantoni;Farhad Farokhi;
      Pages: 4115 - 4122
      Abstract: A structured preconditioned conjugate gradient (PCG) based solver is developed for implementing the Newton updates in second-order methods for a class of constrained network optimal control problems. Of specific interest are problems with discrete-time dynamics arising from the path-graph interconnection of $N$ heterogeneous subsystems. The arithmetic complexity of each PCG step is $O(NT)$, where $T$ is the length of the time horizon. The proposed preconditioning involves a fixed number of block Jacobi iterations per PCG step. A decreasing analytic bound on the effective conditioning is given in terms of this number. The associated computations are decomposable across the spatial and temporal dimensions of the optimal control problem, into subproblems of size independent of $N$ and $T$. Numerical results are provided for two example systems.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • Noisy-Output-Based Direct Learning Tracking Control With Markov Nonuniform
           Trial Lengths Using Adaptive Gains

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      Authors: Dong Shen;Samer S. Saab;
      Pages: 4123 - 4130
      Abstract: In this article, a noisy-output-based direct learning tracking control is proposed for stochastic linear systems with nonuniform trial lengths. The iteration-varying trial length is modeled using a Markov chain for demonstration of the iteration dependence. The effect of the noisy output is asymptotically eliminated using a prior given decreasing gain sequence in the learning algorithm. Two alternative adaptive gains are presented for improving the tracking performance and the convergence speed. Both the mean-square and almost-sure convergence are provided. Numerical simulations on a four-degree-of-freedom robot arm are presented to illustrate the effectiveness of the proposed scheme.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • Optimization Over Time-Varying Networks and Unbounded Information Delays

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      Authors: Arunselvan Ramaswamy;Adrian Redder;Daniel E. Quevedo;
      Pages: 4131 - 4137
      Abstract: Solving optimization problems in multiagent systems involves information exchange between agents. The obtained solutions should be robust to information delays and errors that arise from an unreliable wireless network, which typically connects the multiagent system. In today’s large-scale dynamic Internet of Things style multiagent scenarios, the network topology changes and evolves over time. In this article, we present a simple distributed gradient-based optimization framework and an associated algorithm. Convergence to a minimum of a given objective is shown under mild conditions on the network topology and objective. A key feature of our approach is that we merely assume that the messages sent reach the intended receiver, possibly delayed, with some positive probability. To the best of authors’ knowledge, ours is the first analysis under such weak general network conditions. We also discuss in detail the verifiability of the assumptions involved. This article also makes a technical contribution in terms of the allowed class of objective functions. Specifically, we present an analysis wherein the objective function is such that its sample-gradient is merely locally Lipschitz continuous. The theory developed herein is supported by empirical results.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • Event-Triggered Fixed-Time Attitude Consensus With Fixed and Switching
           Topologies

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      Authors: Xin Jin;Yang Shi;Yang Tang;Herbert Werner;Jürgen Kurths;
      Pages: 4138 - 4145
      Abstract: In this article, event-triggered attitude consensus is considered for multiagent systems with guaranteed fixed-time convergence. Due to the non-Euclidean property of the attitude configuration space, the attitude consensus is more challenging to achieve under the sampled-data setting. An event-triggered attitude consensus protocol and event-triggered condition are proposed based on the axis–angle attitude representation. The fixed-time attitude consensus is reached if the initial attitudes lie in local regions on the attitude configuration space. The theoretical results reveal that the settling time is related to the interevent interval and the algebraic connectivity of the topology graph. We further consider the consensus protocol under a jointly connected graph, and establish the settling time estimation that depends on the switching instants. Numerical simulations are conducted to verify the validity of the theoretical results finally.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • Model Evaluation of the Stochastic Boolean Control Networks

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      Authors: Hongwei Chen;Zidong Wang;Bo Shen;Jinling Liang;
      Pages: 4146 - 4153
      Abstract: This article investigates the model evaluation problem for the stochastic Boolean control networks (SBCNs). First, an algebraic expression of the SBCN is obtained based on the semitensor product method, and a straightforward approach is then proposed to compute the probability that the given observed output sequence is produced by the considered model. Second, two recursive algorithms, namely the forward and the backward algorithms, are designed for model evaluation by resorting to the forward-backward technique. In addition, scaling factors are introduced to deal with the numerical issues arising in the implementation of the developed algorithms. Finally, to illustrate the applicability and effectiveness of the proposed algorithms, a Boolean model of the lac operon is employed as an example for numerical simulation.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • Toward a Convex Design Framework for Online Active Fault Diagnosis of LPV
           Systems

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      Authors: Junbo Tan;Sorin Olaru;Feng Xu;Xueqian Wang;
      Pages: 4154 - 4161
      Abstract: This article focuses on the design of online optimal input sequence for robust active fault diagnosis of discrete-time linear parameter-varying systems using set-theoretic methods. Instead of the traditional set-separation constraint conditions leading to the design of offline input sequence, the proposed approach focuses on online (re)shaping of the input sequence based on the real-time information of the output to discriminate system modes at each time instant such that the diagnosability of system has potential to be further improved. The criterion on the design of optimal input is characterized based on a nonconvex fractional programming problem at each time instant, which is shown to be efficiently solved within a convex optimization framework. In addition to this main contribution, by exploiting Lagrange duality, the optimal input is explicitly obtained by solving a characteristic equation. At the end, a physical circuit model is provided to illustrate the effectiveness of the proposed method.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • SensorsDesign for Large-Scale Boolean Networks via Pinning Observability

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      Authors: Shiyong Zhu;Jianquan Lu;Jie Zhong;Yang Liu;Jinde Cao;
      Pages: 4162 - 4169
      Abstract: In this article, a set of sensors is constructed via the pinning observability approach with the help of observability criteria given in [1] and [2], in order tomake the given Boolean network (BN)be observable. Given the assumption that system states can be accessible, an efficient pinning control scheme is developed to generate an observable BN by adjusting the network structure rather than just to check system observability. Accordingly, the sensors are constructed, of which the form is consistent with that of state feedback controllers in the designed pinning control. Since this pinning control approach only utilizes node-to-node message communication instead of global state space information, the time complexity is dramatically reduced from $O(2^{2n})$ to $O(n^2+n2^d)$, where $n, {text{and}}, d$ are respectively the node number of the considered BN and the largest in-degree of vertices in its network structure. Finally, we design the sensors for the reduced D. melanogaster segmentation polarity gene network and the T-cell receptor kinetics, respectively.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • Transporting Robotic Swarms Via Mean-Field Feedback Control

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      Authors: Tongjia Zheng;Qing Han;Hai Lin;
      Pages: 4170 - 4177
      Abstract: With the rapid development of Artificial Intelligence (AI) and robotics, deploying a large swarm of networked robots has foreseeable applications in the near future. Existing research in swarm robotics has mainly followed a bottom-up philosophy with predefined local coordination and control rules. However, it is arduous to verify the global requirements and analyze their performance. This motivates us to pursue a top–down approach, and develop a provable control strategy for transporting a robotic swarm to achieve a desired global configuration. Specifically, we use mean-field partial differential equations (PDEs) to model the swarm and control its mean-field density (i.e., probability density) over a bounded spatial domain using mean-field feedback. The presented control law uses density estimates as feedback signals and generates corresponding velocity fields that, by acting locally on individual robots, guide their global distribution to a target profile. The design of the velocity field is therefore centralized, but the implementation of the controller can be fully distributed—individual robots sense the velocity field and derive their own velocity control signals accordingly. The key contribution lies in applying the concept of input-to-state stability (ISS) to show that the perturbed closed-loop system (a nonlinear and time-varying PDE) is locally ISS with respect to density estimation errors. The effectiveness of the proposed control laws is verified using agent-based simulations.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • Necessary and Sufficient Conditions for Fault Diagnosability of Linear
           

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      Authors: Qinyuan Liu;Zidong Wang;Junfeng Zhang;Donghua Zhou;
      Pages: 4178 - 4185
      Abstract: In this article, the diagnosability problem is investigated for open-loop and closed-loop stochastic systems under sensor and actuator faults. A novel quantitative fault diagnosability analysis method is developed to characterize the detectability and isolability for a given system without any predetermined fault diagnosis algorithm. By exploiting the Bhattacharyya distance, some novel indices are proposed to evaluate the difficulty degree of fault detectability and isolability. Necessary and sufficient conditions are established for the diagnosability of both open- and closed-loop stochastic systems undergoing sensor and actuator faults. In addition, the relationship between the fault detectability of open-loop systems and that of closed-loop ones is discussed.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • Codesign of Dynamic Event-Triggered Gain-Scheduling Control for a Class of
           Nonlinear Systems

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      Authors: Pedro Henrique Silva Coutinho;Reinaldo Martínez Palhares;
      Pages: 4186 - 4193
      Abstract: This technical article investigates the problem of event-triggered gain-scheduling control of nonlinear systems. The considered class of nonlinear systems is such that an equivalent local polytopic model is obtained. By following the codesign approach, it is performed the simultaneous design of the dynamic event-triggering mechanism (ETM) and the gain-scheduled controller parameterized in terms of state-dependent scheduling functions. As the state information is only available to the controller at specific instants determined by the ETM, the scheduling functions of controller and plant polytopic model are asynchronous. To cope with this drawback, the trigger function of the dynamic ETM is appropriately defined to mitigate the influence of asynchronous scheduling functions in the Lyapunov stability-based analysis, allowing to derive a less conservative codesign condition. A convex optimization problem with linear matrix inequality constraints is proposed to perform the codesign and to enlarge the interevent times. Also, it is shown that there exists a lower bound for the interevent times, which prevents Zeno behavior. Numerical examples are provided to illustrate the advantages of the proposed dynamic event-triggered control codesign approach over emulation-based approach and its static counterpart.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • Linear Convergence of First- and Zeroth-Order Primal–Dual Algorithms for
           Distributed Nonconvex Optimization

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      Authors: Xinlei Yi;Shengjun Zhang;Tao Yang;Tianyou Chai;Karl H. Johansson;
      Pages: 4194 - 4201
      Abstract: This article considers the distributed nonconvex optimization problem of minimizing a global cost function formed by a sum of local cost functions by using local information exchange. We first consider a distributed first-order primal–dual algorithm. We show that it converges sublinearly to a stationary point if each local cost function is smooth and linearly to a global optimum under an additional condition that the global cost function satisfies the Polyak–Łojasiewicz condition. This condition is weaker than strong convexity, which is a standard condition for proving linear convergence of distributed optimization algorithms, and the global minimizer is not necessarily unique. Motivated by the situations where the gradients are unavailable, we then propose a distributed zeroth-order algorithm, derived from the considered first-order algorithm by using a deterministic gradient estimator, and show that it has the same convergence properties as the considered first-order algorithm under the same conditions. The theoretical results are illustrated by numerical simulations.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • An Adaptive Control Framework for Underactuated Switched
           Euler–Lagrange Systems

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      Authors: Spandan Roy;Simone Baldi;Petros A. Ioannou;
      Pages: 4202 - 4209
      Abstract: The control of underactuated Euler–Lagrange systems with uncertain and switched parameters is an important problem whose solution has many applications. The problem is challenging as standard adaptive control techniques do not extend to this class of systems due to structural constraints that lead to parameterization difficulties. This note proposes an adaptive switched control framework that handles the uncertainty and switched dynamics without imposing structural constraints. A case study inspired by autonomous vessel operations is used to show the effectiveness of the proposed approach.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • Data-Driven Stabilization of Nonlinear Polynomial Systems With Noisy Data

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      Authors: Meichen Guo;Claudio De Persis;Pietro Tesi;
      Pages: 4210 - 4217
      Abstract: In a recent article, we have shown how to learn controllers for unknown linear systems using finite-length noisy data by solving linear matrix inequalities. In this article, we extend this approach to deal with unknown nonlinear polynomial systems by formulating stability certificates in the form of data-dependent sum of squares programs, whose solution directly provides a stabilizing controller and a Lyapunov function. We then derive variations of this result that lead to more advantageous controller designs. The results also reveal connections to the problem of designing a controller starting from a least-square estimate of the polynomial system.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • Adaptive Event-Triggered Control of Uncertain Nonlinear Systems Using
           Intermittent Output Only

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      Authors: Zhirong Zhang;Changyun Wen;Lantao Xing;Yongduan Song;
      Pages: 4218 - 4225
      Abstract: Although rich collection of research results on event-triggered control exist, no effort has ever been made in integrating state/output triggering and controller triggering simultaneously with backstepping control design. The primary objective of this article is, by using intermittent output signal only, to build a backstepping adaptive event-triggered feedback control for a class of uncertain nonlinear systems. To do so, we need to tackle three technical obstacles. First, the nature of the event triggering makes the transmitted output signal discontinuous, rendering the regular recursive backstepping design method inapplicable as the repetitive differentiation of virtual control signals is literally undefined. Second, the effects arisen from the event-triggering action must be properly accommodated, but the current compensating method only works for systems in normal form, thus a new method needs to be developed in order to handle nonnormal form systems. Third, as only intermittent output signal is available, and at the same time, the impacts of certain terms containing unknown parameters (arising from event triggering) need to be compensated, it is rather challenging to design a suitable state observer. To circumvent these difficulties, we employ the dynamic filtering technique to avoid the differentiation of virtual control signals in the backstepping design, construct a new compensation scheme to deal with the effects of output triggering, and build a new form of state observer to facilitate the development of output feedback control. It is shown that, with the derived adaptive backstepping output-triggered control, all the closed-loop signals are ensured bounded and the transient system performance in the mean square error sense can be adjusted by appropriately adjusting design parameters. The benefits and effectiveness of the proposed scheme are also validated by numerical simulation.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • Resilient Event-Triggered Control Strategies for Second-Order Consensus

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      Authors: Wenying Xu;Jürgen Kurths;Guanghui Wen;Xinghuo Yu;
      Pages: 4226 - 4233
      Abstract: This article investigates the second-order consensus issue for multiagent systems subject to both limited communication resources and replay attacks. First, an asynchronous dynamic edge event-triggered (DEET) communication scheme is developed to reduce the utilization of network resources in the absence of attacks. Then, we further consider the case of replay attacks launched by multiple adversaries, under which the transmitted information is maliciously replaced by a previous unnecessary message. To overcome the impact caused by replay attacks, a modified DEET scheme and an effective attack-resilient consensus protocol are well constructed, both of which successfully guarantee second-order consensus in the presence of replay attacks. In addition, internal dynamic variables are utilized in the proposed DEET schemes such that the triggering time sequence does not exhibit Zeno behavior. Finally, one numerical example is provided to illustrate our theoretical analysis.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • Stabilization of Discrete-Time Hidden Semi-Markov Jump Singularly
           Perturbed Systems With Partially Known Emission Probabilities

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      Authors: Feng Li;Wei Xing Zheng;Shengyuan Xu;
      Pages: 4234 - 4240
      Abstract: This article considers the stabilization problem of discrete-time semi-Markov jump singularly perturbed systems, in which the system operation mode is hidden but can be estimated by a detector with some emission probabilities. To model this circumstance, the hidden semi-Markov model (HSMM) with partially known emission probabilities is introduced, where the hidden state represents the real system operation mode while the emitted value represents the estimated value of the system operation mode and is available for the stabilizing controller. Based on the introduced HSMM, the stabilizing controller which only relies on the estimated value is designed to guarantee the $delta$-error mean square stability of the closed-loop system. A numerical example is used to verify the usefulness of the obtained results.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • Generalized Second-Order Value Iteration in Markov Decision Processes

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      Authors: Chandramouli Kamanchi;Raghuram Bharadwaj Diddigi;Shalabh Bhatnagar;
      Pages: 4241 - 4247
      Abstract: Value iteration is a fixed point iteration technique utilized to obtain the optimal value function and policy in a discounted reward Markov decision process (MDP). Here, a contraction operator is constructed and applied repeatedly to arrive at the optimal solution. Value iteration is a first-order method and, therefore, it may take a large number of iterations to converge to the optimal solution. Successive relaxation is a popular technique that can be applied to solve a fixed point equation. It has been shown in the literature that under a special structure of the MDP, successive overrelaxation technique computes the optimal value function faster than standard value iteration. In this article, we propose a second-order value iteration procedure that is obtained by applying the Newton–Raphson method to the successive relaxation value iteration scheme. We prove the global convergence of our algorithm to the optimal solution asymptotically and show the second-order convergence. Through experiments, we demonstrate the effectiveness of our proposed approach.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • Provably Robust Verification of Dissipativity Properties from Data

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      Authors: Anne Koch;Julian Berberich;Frank Allgöwer;
      Pages: 4248 - 4255
      Abstract: Dissipativity properties have proven to be very valuable for systems analysis and controller design. With the rising amount of available data, there has, therefore, been an increasing interest in determining dissipativity properties from (measured) trajectories directly, while an explicit model of the system remains undisclosed. Most existing approaches for data-driven dissipativity, however, guarantee the dissipativity condition only over a finite-time horizon and provide weak or no guarantees on robustness in the presence of noise. In this article, we present a framework for verifying dissipativity properties from measured data with desirable guarantees. We first consider the case of input-state measurements, where we provide computationally attractive conditions in the presence of process noise. We extend this approach to input–output data, where similar results hold in the noise-free case, and finally provide results for the case of noisy input–output trajectories.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • Diffusion-Based Distributed Parameter Estimation Through Directed Graphs
           With Switching Topology: Application of Dynamic Regressor Extension and
           Mixing

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      Authors: Alexey S. Matveev;Mostafa Almodarresi;Romeo Ortega;Anton Pyrkin;Siyu Xie;
      Pages: 4256 - 4263
      Abstract: In this article, we consider the problem of discrete-time, diffusion-based distributed parameter estimation with the agents connected via directed graphs with switching topologies and a self loop at each node. We show that, by incorporating the recently introduced dynamic regressor extension and mixing procedure to a classical gradient-descent algorithm, improved convergence properties can be achieved. In particular, it is shown that with this modification sufficient conditions for global convergence of all the estimators is that one of the sensors receives enough information to generate a consistent estimate and that this sensor is “well-connected.” The main feature of this result is that the excitation condition imposed on this distinguished sensor is strictly weaker than the classical persistent excitation requirement. The connectivity assumption is also very mild, requiring only that the union of the edges of all connectivity graphs over any time interval with an arbitrary but fixed length contains a spanning tree rooted at the information-rich node. In the case of nonswitching topologies, this assumption is satisfied by strongly connected graphs, and not only by them.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • Distributed Primal–Dual Splitting Algorithm for Multiblock
           Separable Optimization Problems

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      Authors: Huaqing Li;Xiangzhao Wu;Zheng Wang;Tingwen Huang;
      Pages: 4264 - 4271
      Abstract: This article considers the distributed structured optimization problem of collaboratively minimizing the global objective function composed of the sum of local cost functions. Each local objective function involves a Lipschitz-differentiable convex function, a nonsmooth convex function, and a linear composite nonsmooth convex function. For such problems, we derive the synchronous distributed primal–dual splitting (S-DPDS) algorithm with uncoordinated stepsizes. Meanwhile, we develop the asynchronous version of the algorithm in light of the randomized block-coordinate method (A-DPDS). Further, the convergence results show the relaxed range and concise form of the acceptable parameters, which indicates that the algorithms are conducive to the selection of parameters in practical applications. Finally, we demonstrate the efficiency of S-DPDS and A-DPDS algorithms by numerical experiments.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • On Heavy-Traffic Optimal Scaling of c-Weighted MaxWeight Scheduling in
           Input-Queued Switches

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      Authors: Yingdong Lu;Siva Theja Maguluri;Mark S. Squillante;Tonghoon Suk;
      Pages: 4272 - 4277
      Abstract: We consider the asymptotically optimal control of input-queued switches under a cost-weighted variant of MaxWeight scheduling, for which we establish theoretical properties that include showing the algorithm exhibits optimal heavy-traffic queue-length scaling. Our results are expected to be of theoretical interest more broadly than input-queued switches.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • Distributed Estimation of Time-Varying Biases in Relative State
           Measurements

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      Authors: Mingming Shi;
      Pages: 4278 - 4284
      Abstract: This article focuses on the distributed estimation of time-varying bias signals in relative state measurements of sensors, where each sensor measures the relative state of neighboring sensors and the measurements contain a time-varying bias signal that is generated from a linear exo-systems. Assume that sensors can communicate with others, we propose several distributed bias estimators, by which each sensor can reconstruct its own bias signal and the exo-system’s state using local information.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • Stability Analysis of Gradient-Based Distributed Formation Control With
           Heterogeneous Sensing Mechanism: The Three Robot Case

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      Authors: Nelson P. K. Chan;Bayu Jayawardhana;Hector Garcia de Marina;
      Pages: 4285 - 4292
      Abstract: This article focuses on the stability analysis of a formation shape displayed by a team of mobile robots that uses a heterogeneous sensing mechanism. For the setups consisting of three robots, we show that the use of heterogeneous gradient-based control laws can give rise to undesired invariant sets where a distorted formation shape is possibly moving at a constant velocity. We guarantee local asymptotic stability for the correct and desired formation shape. For the setup with one distance and two bearing robots, we identify the conditions such that an incorrect moving formation is locally attractive.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • A Note on Order and Index Reduction for Descriptor Systems

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      Authors: Martin J. Corless;Robert N. Shorten;
      Pages: 4293 - 4299
      Abstract: We present order reduction results for linear time invariant descriptor systems. Results are given for both forced and unforced systems as well methods for constructing the reduced order systems. Our results establish a precise connection between classical and new results on this topic, and lead to an elementary construction of quasi-Weierstrass forms for a descriptor system. Examples are given to illustrate the usefulness of our results.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • Lie Algebraic Unscented Kalman Filter for Pose Estimation

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      Authors: Alexander Meyer Sjøberg;Olav Egeland;
      Pages: 4300 - 4307
      Abstract: An unscented Kalman filter (UKF) for matrix Lie groups is proposed where the time propagation of the state is formulated on the Lie algebra. This is done with the kinematic differential equation of the logarithm, where the inverse of the right Jacobian is used. The sigma points can then be expressed as logarithms in vector form, and time propagation of the sigma points and the computation of the mean and the covariance can be done on the Lie algebra. The resulting formulation is to a large extent based on logarithms in vector form and is, therefore, closer to the UKF for systems in $mathbb {R}^n$. This gives an elegant and well-structured formulation, which provides additional insight into the problem, and which is computationally efficient. The proposed method is in particular formulated and investigated on the matrix Lie group $SE(3)$. A discussion on right and left Jacobians is included, and a novel closed-form solution for the inverse of the right Jacobian on $SE(3)$ is derived, which gives a compact representation involving fewer matrix operations. The proposed method is validated in simulations.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • $k$ -Positive+Linear+Systems+With+Applications+to+Nonlinear+Systems&rft.title=IEEE+Transactions+on+Automatic+Control&rft.issn=0018-9286&rft.date=2022&rft.volume=67&rft.spage=4308&rft.epage=4313&rft.aulast=Margaliot;&rft.aufirst=Chengshuai&rft.au=Chengshuai+Wu;Michael+Margaliot;">Diagonal Stability of Discrete-Time $k$ -Positive Linear Systems With
           Applications to Nonlinear Systems

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      Authors: Chengshuai Wu;Michael Margaliot;
      Pages: 4308 - 4313
      Abstract: A linear dynamical system is called $k$-positive if its dynamics maps the set of vectors with up to $k-1$ sign variations to itself. For $k=1$, this reduces to the important class of positive linear systems. Since stable positive linear time-invariant systems always admit a diagonal quadratic Lyapunov function, i.e., they are diagonally stable, we may expect that this holds also for stable $k$-positive systems. We show that, in general, this is not the case both in the continuous-time and discrete-time (DT) case. We then focus on DT $k$-positive linear systems and introduce the new notion of the DT $k$-diagonal stability. It is shown that this is a necessary condition for the standard DT diagonal stability. We demonstrate an application of this new notion to the analysis of a class of DT nonlinear systems.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • Multiple Sparsity Constrained Control Node Scheduling With Application to
           Rebalancing of Mobility Networks

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      Authors: Takuya Ikeda;Kazunori Sakurama;Kenji Kashima;
      Pages: 4314 - 4321
      Abstract: This article treats an optimal scheduling problem of control nodes in networked systems. We newly introduce both the $L^0$ and $ell ^0$ constraints on control inputs to extract a time-varying small number of effective control nodes. As the cost function, we adopt the trace of the controllability Gramian to reduce the required control energy. Since the formulated optimization problem is combinatorial, we introduce a convex relaxation problem for its computational tractability. After a reformulation of the problem into an optimal control problem to which Pontryagin’s maximum principle is applicable, we give a sufficient condition under which the relaxed problem gives a solution of the main problem. Finally, the proposed method is applied to a rebalancing problem of a mobility network.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • Distributed Global Optimization for a Class of Nonconvex Optimization With
           Coupled Constraints

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      Authors: Xiaoxing Ren;Dewei Li;Yugeng Xi;Haibin Shao;
      Pages: 4322 - 4329
      Abstract: This article examines the distributed nonconvex optimization problem with structured nonconvex objective functions and coupled convex inequality constraints on static networks. A distributed continuous-time primal-dual algorithm is proposed to solve the problem. We use the canonical transformation and Lagrange multiplier method to reformulate the nonconvex optimization problem as a convex–concave saddle point computation problem, which is subsequently solved by employing the projected primal-dual subgradient method. Sufficient conditions that guarantee the global optimality of the solution generated by the proposed algorithm are provided. Numerical and application examples are presented to demonstrate the proposed algorithm.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • Matrix Decomposition-Based Adaptive Control of Noncanonical Form MIMO DT
           Nonlinear Systems

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      Authors: Yanjun Zhang;Ji-Feng Zhang;Xiao-Kang Liu;
      Pages: 4330 - 4337
      Abstract: This article presents a new study on adaptive control of multi-input and multi-output (MIMO) discrete-time nonlinear systems with a noncanonical form involving parametric uncertainties. The adaptive control scheme employs a vector relative degree formulation to reconstruct the noncanonical system dynamics and derives a normal form. Then, a new matrix decomposition-based adaptive control scheme is proposed for the controlled plant with a vector relative degree $[1, 1,{ldots }, 1]$ under some relaxed design conditions. In particular, the matrix decomposition technique is adopted to overcome the singularity problem during the adaptive estimation of an uncertain high-frequency gain matrix. The adaptive control scheme ensures closed-loop stability and asymptotic output tracking. An extension to the adaptive control of general canonical-form MIMO discrete-time nonlinear systems is also presented. Finally, through simulations, the effectiveness of the proposed control scheme is verified.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • Optimal Consensus via OCPI Regulation for Unknown Pure-Feedback Agents
           With Disturbances and State Delays

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      Authors: Athanasios K. Gkesoulis;Haris E. Psillakis;Athanasios-Rafail Lagos;
      Pages: 4338 - 4345
      Abstract: In this article, we present a novel methodology to address the optimal output consensus problem for multiagent systems. We propose a distributed variable transformation that recasts the aforementioned problem into a simpler regulation problem of the new variables. The transformation requires only relative output measurements and is independent of any underlying agent dynamics and therefore applicable to a general class of systems. Classical control techniques, which take into account the agent dynamics and only need the relative outputs to be shared between neighbors, can then be employed to regulate the new variables. We utilize the methodology to address, for the first time, the problem of optimal output consensus for unknown pure-feedback agents with disturbances and state delays. Simulation studies on a group of continuous stirred tank reactors illustrate the validity of the theoretical analysis.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • Protocol-Based Output-Feedback Control for Semi-Markov Jump Systems

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      Authors: Jun Cheng;Lifei Xie;Ju H. Park;Huaicheng Yan;
      Pages: 4346 - 4353
      Abstract: This study addresses the output-feedback control problem for general semi-Markov jump systems with a memory-dynamic event-triggered protocol. Based on the average dwell-time strategy, a novel framework of semi-Markov process subject to a higher level deterministic switching signal is constructed, where the Markov renewal process is nonhomogeneous. Aiming at improving communication efficiency and control performance, a novel dynamic-memory event-triggered protocol is proposed, and the resulting system can be transformed into a time-delay system. By considering an asynchronous memory-based output-feedback control law, whose asynchronization can be modeled by a nonhomogeneous hidden semi-Markov model, some parameter-dependent sufficient criteria are forwarded to ensure the mean-square exponential stability of the closed-loop system. Finally, the effectiveness and applicability of the presented approach are testified by two examples.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • Statistical Similarity Measure-Based Adaptive Outlier-Robust State
           Estimator With Applications

    • Free pre-print version: Loading...

      Authors: Mingming Bai;Yulong Huang;Yonggang Zhang;Jonathon Chambers;
      Pages: 4354 - 4361
      Abstract: This article presents an adaptive outlier-robust state estimator (AORSE) under the statistical similarity measures (SSMs) framework. Two SSMs are first proposed to evaluate the similarities between a pair of positive definite random matrices and between a pair of weighted random vectors, respectively. The AORSE is developed by maximizing a hybrid SSMs based cost function, wherein the posterior density function of the hidden state is assumed as a Gaussian distribution with the posterior covariance being approximately determined in a heuristic way. Simulation and experimental examples of moving-target tracking demonstrate the effectiveness of the proposed algorithm.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • $^{T}$ +Updates&rft.title=IEEE+Transactions+on+Automatic+Control&rft.issn=0018-9286&rft.date=2022&rft.volume=67&rft.spage=4362&rft.epage=4369&rft.aulast=Axehill;&rft.aufirst=Daniel&rft.au=Daniel+Arnström;Alberto+Bemporad;Daniel+Axehill;">A Dual Active-Set Solver for Embedded Quadratic Programming Using
           Recursive LDL $^{T}$ Updates

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      Authors: Daniel Arnström;Alberto Bemporad;Daniel Axehill;
      Pages: 4362 - 4369
      Abstract: In this technical article, we present a dual active-set solver for quadratic programming that has properties suitable for use in embedded model predictive control applications. In particular, the solver is efficient, can easily be warm started, and is simple to code. Moreover, the exact worst-case computational complexity of the solver can be determined offline and, by using outer proximal-point iterations, ill-conditioned problems can be handled in a robust manner.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • Stochastic Filtering Scheme of Implicit Forms of Uncertain Max-Plus Linear
           Systems

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      Authors: Guilherme Espindola-Winck;Laurent Hardouin;Mehdi Lhommeau;Rafael Santos-Mendes;
      Pages: 4370 - 4376
      Abstract: This article aims to improve the stochastic filtering algorithm with bounded disturbances, proposed in 1. This filter is efficient for max-plus linear systems in explicit form, i.e., the timed event graph (TEG) described by this system is initially with one token on each place. Nevertheless, it needs strong assumptions in order to be accurate for systems in implicit form, i.e., the corresponding TEG is initially with some token-free places which implies that some entries of the system state vector are dependent on each other. In this article, we consider a more general method without these assumptions. It is based on an iterative procedure that widely increases the accuracy of the estimation.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • “Second-Order Primal” + “First-Order Dual” Dynamical Systems With
           

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      Authors: Xin He;Rong Hu;Ya-Ping Fang;
      Pages: 4377 - 4383
      Abstract: Second-order dynamical systems are important tools for solving optimization problems, and most of the existing works in this field have focused on unconstrained optimization problems. In this article, we propose an inertial primal–dual dynamical system with constant viscous damping and time scaling for the linear equality constrained convex optimization problem, which consists of a second-order ordinary differential equation (ODE) for the primal variable and a first-order ODE for the dual variable. When the scaling satisfies certain conditions, we prove its convergence property without assuming strong convexity. Even the convergence rate can become exponential when the scaling grows exponentially. We also show that the obtained convergence property of the dynamical system is preserved under a small perturbation.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • Erratum to “A Contraction Approach to the Hierarchical Analysis and
           Design of Networked Systems” [May 13 1328-1331]

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      Authors: Giovanni Russo;Mario di Bernardo;Eduardo D. Sontag;
      Pages: 4384 - 4384
      Abstract: A small correction is made in the example of Section V (A representative application to networked systems) of the paper with the above title.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • Comments on “Representation and Factorization of Discrete-Time Rational
           All-Pass Functions”

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      Authors: Augusto Ferrante;Giorgio Picci;
      Pages: 4385 - 4385
      Abstract: This article answers a system-theoretic conjecture proposed by Ferrante and Picci (2017). A counterexample to such conjecture is presented along with a modification of the statement, which preserves the essence of the result and is proven to be true.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • Corrections to “Set-Membership Filter for Discrete-Time Nonlinear
           Systems Using State-Dependent Coefficient Parameterization” [Feb 22
           894-901]

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      Authors: Diganta Bhattacharjee;Kamesh Subbarao;
      Pages: 4386 - 4386
      Abstract: Some of the final sets of corrections requested in the proof did not unfortunately make it to the final version in [1]. We would like to point those out in this document.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • Expand Your Network, Get Rewarded

    • Free pre-print version: Loading...

      Pages: 4387 - 4387
      Abstract: Advertisement.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
  • Introducing IEEE Collabratec

    • Free pre-print version: Loading...

      Pages: 4388 - 4388
      Abstract: Advertisement.
      PubDate: Aug. 2022
      Issue No: Vol. 67, No. 8 (2022)
       
 
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