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IEEE Transactions on Automatic Control
Journal Prestige (SJR): 3.433
Citation Impact (citeScore): 6
Number of Followers: 70  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0018-9286
Published by IEEE Homepage  [228 journals]
  • IEEE Control Systems Society Information

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      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • IEEE Control Systems Society Information

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      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • About Robustness of Control Systems Embedding an Internal Model

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      Authors: Michelangelo Bin;Daniele Astolfi;Lorenzo Marconi;
      Pages: 1306 - 1320
      Abstract: Robustness is a basic property of any control system. In the context of linear output regulation, it was proved that embedding an internal model of the exogenous signals is necessary and sufficient to achieve tracking of the desired reference signals in spite of external disturbances and parametric uncertainties. This result is commonly known as the internal model principle. A complete extension of such linear result to general nonlinear systems is still an open problem, exacerbated by the large number of alternative definitions of uncertainty and desired control goals that are possible in a nonlinear setting. In this article, we develop a general framework in which all these different notions can be formally characterized in a unifying way. Classical results are reinterpreted in the proposed setting, and new results and insights are presented with a focus on robust rejection/tracking of arbitrary harmonic content. Moreover, we show by counterexample that, in the relevant case of continuous unstructured uncertainties, there are problems for which no smooth finite-dimensional robust regulator exists ensuring exact regulation.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • Safe Policies for Reinforcement Learning via Primal-Dual Methods

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      Authors: Santiago Paternain;Miguel Calvo-Fullana;Luiz F. O. Chamon;Alejandro Ribeiro;
      Pages: 1321 - 1336
      Abstract: In this article, we study the design of controllers in the context of stochastic optimal control under the assumption that the model of the system is not available. This is, we aim to control a Markov decision process of which we do not know the transition probabilities, but we have access to sample trajectories through experience. We define safety as the agent remaining in a desired safe set with high probability during the operation time. The drawbacks of this formulation are twofold. The problem is nonconvex and computing the gradients of the constraints with respect to the policies is prohibitive. Hence, we propose an ergodic relaxation of the constraints with the following advantages. 1) The safety guarantees are maintained in the case of episodic tasks and they hold until a given time horizon for continuing tasks. 2) The constrained optimization problem despite its nonconvexity has arbitrarily small duality gap if the parametrization of the controller is rich enough. 3) The gradients of the Lagrangian associated with the safe learning problem can be computed using standard reinforcement learning results and stochastic approximation tools. Leveraging these advantages, we exploit primal-dual algorithms to find policies that are safe and optimal. We test the proposed approach in a navigation task in a continuous domain. The numerical results show that our algorithm is capable of dynamically adapting the policy to the environment and the required safety levels.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • A Robust Distributed Interval Observer for LTI Systems

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      Authors: Xiaoling Wang;Housheng Su;Fan Zhang;Guanrong Chen;
      Pages: 1337 - 1352
      Abstract: In this article, the state estimation problem of a continuous-time linear time-invariant system is investigated for the situation with unknown external disturbance and measurement noise. A robust distributed interval observer is designed, which consists of a group of sensors communicating with others through a directed graph where each sensor can only access partial information from the output of the plant. The communication among the sensors together with the heterogeneity and undetectability of the sensors result in some stringent requirements on the robust distributed interval observer construction. To resolve these restrictions, the internally positive representation originated from a single agent system is introduced into the robust distributed interval observer. After presenting detailed design and analysis, numerical simulations are demonstrated to verify the theoretical results.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • Topological Analysis of Vector-Field Guided Path Following on Manifolds

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      Authors: Weijia Yao;Bohuan Lin;Brian D. O. Anderson;Ming Cao;
      Pages: 1353 - 1368
      Abstract: A path-following control algorithm enables a system’s trajectories under its guidance to converge to and evolve along a given geometric desired path. There exist various such algorithms, but many of them can only guarantee local convergence to the desired path in its neighborhood. In contrast, the control algorithms using a well-designed guiding vector field can ensure almost global convergence of trajectories to the desired path; here, “almost” means that in some cases, a measure-zero set of trajectories converge to the singular set where the vector field becomes zero (with all other trajectories converging to the desired path). In this article, we first generalize the guiding vector field from the Euclidean space to a general smooth Riemannian manifold. This generalization can deal with path-following in some abstract configuration space (such as robot arm joint space). Then, we show several theoretical results from a topological viewpoint. Specifically, we are motivated by the observation that singular points of the guiding vector field exist in many examples where the desired path is homeomorphic to the unit circle, but it is unknown whether the existence of singular points always holds in general (i.e., is inherent in the topology of the desired path). In the $n$-dimensional Euclidean space, we provide an affirmative answer, and conclude that it is not possible to guarantee global convergence to desired paths that are homeomorphic to the unit circle. Furthermore, we show that there always exist nonpath-converging trajectories (i.e., trajectories that do not converge to the desired path) starting from the boundary of a ball containing the desired path in an $n$-dimensional Euclidean space where $n geq 3$. Examples are provided to illustrate the theoretical results.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • Adaptive Regulation of Block-Oriented Nonlinear Systems Using Binary
           Sensors With Applications to Automotive Engine Control

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      Authors: Wenxiao Zhao;Erik Weyer;George Yin;Daoyi Dong;Yahui Zhang;Tielong Shen;
      Pages: 1369 - 1382
      Abstract: In this article, adaptive regulation of block-oriented nonlinear systems, i.e., Hammerstein and Wiener systems, with binary-valued measurements of the regulation errors is considered. Compared with the classical framework for stochastic adaptive control, the new feature here is that only binary-valued observations of regulation errors are available to the controller. An adaptive regulator based on the stochastic approximation algorithm is proposed and it is proved that the regulator is optimal in the sense that it minimizes the long-run average of the squared regulation errors almost surely. Numerical examples as well as real applications of the proposed algorithms to automotive engine control are given.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • Superconvergence of Online Optimization for Model Predictive Control

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      Authors: Sen Na;Mihai Anitescu;
      Pages: 1383 - 1398
      Abstract: We develop a one-Newton-step-per-horizon, online, lag-$L$, model predictive control (MPC) algorithm for solving discrete-time, equality-constrained, nonlinear dynamic programs. Based on recent sensitivity analysis results for the target problems class, we prove that the approach exhibits a behavior that we call superconvergence; that is, the tracking error with respect to the full-horizon solution is not only stable for successive horizon shifts, but also decreases with increasing shift order to a minimum value that decays exponentially in the length of the receding horizon. The key analytical step is the decomposition of the one-step error recursion of our algorithm into algorithmic error and perturbation error. We show that the perturbation error decays exponentially with the lag between two consecutive receding horizons, whereas the algorithmic error, determined by Newton's method, achieves quadratic convergence instead. Overall this approach induces our local exponential convergence result in terms of the receding horizon length for suitable values of $L$. Numerical experiments validate our theoretical findings.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • Optimal Sensor Scheduling Under Intermittent Observations Subject to
           Network Dynamics

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      Authors: Hassan Hmedi;Johnson Carroll;Ari Arapostathis;
      Pages: 1399 - 1414
      Abstract: Motivated by various distributed control applications, we consider a linear system with Gaussian noise observed by multiple sensors which transmit measurements over a dynamic lossy network. We characterize the stationary optimal sensor scheduling policy for the finite horizon, discounted, and long-term average cost problems and show that the value iteration algorithm converges to a solution of the average cost problem. We further show that the suboptimal policies provided by the rolling horizon truncation of the value iteration also guarantee stability and provide near-optimal average cost. Lastly, we provide qualitative characterizations of the multidimensional set of measurement loss rates for which the system is stabilizable for a static network, thus extending earlier results on intermittent observations.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • Nonlinear Opinion Dynamics With Tunable Sensitivity

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      Authors: Anastasia Bizyaeva;Alessio Franci;Naomi Ehrich Leonard;
      Pages: 1415 - 1430
      Abstract: We propose a continuous-time multioption nonlinear generalization of classical linear weighted-average opinion dynamics. Nonlinearity is introduced by saturating opinion exchanges, and this is enough to enable a significantly greater range of opinion-forming behaviors with our model as compared to existing linear and nonlinear models. For a group of agents that communicate opinions over a network, these behaviors include multistable agreement and disagreement, tunable sensitivity to input, robustness to disturbance, flexible transition between patterns of opinions, and opinion cascades. We derive network-dependent tuning rules to robustly control the system behavior and we design state-feedback dynamics for the model parameters to make the behavior adaptive to changing external conditions. The model provides new means for systematic study of dynamics on natural and engineered networks, from information spread and political polarization to collective decision-making and dynamic task allocation.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • Prescribed-Time Output-Feedback Control of Stochastic Nonlinear Systems

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      Authors: Wuquan Li;Miroslav Krstic;
      Pages: 1431 - 1446
      Abstract: We present prescribed-time output-feedback-stabilizing designs for stochastic nonlinear strict-feedback systems. We first propose a new nonscaling output-feedback control scheme to solve the prescribed-time mean-square stabilization problem for stochastic nonlinear systems without sensor uncertainty. In this case, compared with the existing results on stochastic nonlinear prescribed-time stabilization, an appealing feature in our design is that the order of the scaling function in the controller is dramatically reduced, which yields a simpler controller and with the control effort reduced. We then consider prescribed-time output-feedback control for stochastic nonlinear systems with sensor uncertainty. In this case, the new design ingredient is that a time-varying controller is designed to guarantee prescribed-time mean-square stabilization, different from the existing results where an adaptive controller is designed to ensure almost sure regulation (as time goes to infinity). Finally, two simulation examples are given to illustrate the stochastic nonlinear prescribed-time output-feedback designs.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • A Lyapunov-Based ISS Small-Gain Theorem for Infinite Networks of Nonlinear
           Systems

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      Authors: Christoph Kawan;Andrii Mironchenko;Majid Zamani;
      Pages: 1447 - 1462
      Abstract: In this article, we show that an infinite network of input-to-state stable (ISS) subsystems, admitting ISS Lyapunov functions, itself admits an ISS Lyapunov function, provided that the couplings between the subsystems are sufficiently weak. The strength of the couplings is described in terms of the properties of an infinite-dimensional nonlinear positive operator, built from the interconnection gains. If this operator induces a uniformly globally asymptotically stable (UGAS) system, a Lyapunov function for the infinite network can be constructed. We analyze necessary and sufficient conditions for UGAS and relate them to small-gain conditions used in the stability analysis of finite networks.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • A New Event-Triggered Control Scheme for Stochastic Systems

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      Authors: Hao Yu;Tongwen Chen;Fei Hao;
      Pages: 1463 - 1478
      Abstract: This article studies event-triggered control of stochastic linear discrete-time systems with discounted quadratic cost functions. A new dynamic event-triggering condition is proposed, which has simultaneously stochastic and deterministic features. The designed event-triggered control system ensures the control performance to be within a desirable level relative to that using periodic time-triggered control, while discarding the unnecessary transmissions. By adjusting the parameters, the proposed event-triggering condition can be reduced to some existing ones in the literature. It is shown that the three features (dynamic, stochastic, and deterministic) are all helpful to further increase the average interevent times. Then, the criteria in terms of the parameters are presented to ensure mean-square stability of the closed-loop systems. Moreover, an improved version of the proposed event-triggering condition is given to enlarge the minimum interevent times. Finally, numerical simulations are given to illustrate the efficiency and feasibility of the proposed results.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • Expedited Online Learning With Spatial Side Information

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      Authors: Pranay Thangeda;Melkior Ornik;Ufuk Topcu;
      Pages: 1479 - 1491
      Abstract: The applicability of model-based online reinforcement learning algorithms is often limited by the amount of exploration required for learning the environment model to the desired level of accuracy. A promising approach to addressing this issue is to exploit side information, available either a priori or during the agent’s mission, for learning the unknown dynamics. Side information in our context refers to information in the form of bounds on the differences between transition probabilities at different states in the environment. We use this information as a measure of reusability of the direct experience gained by performing actions and observing the outcomes at different states. We propose a framework to integrate side information into existing model-based reinforcement learning algorithms by complementing the samples obtained directly at states with second-hand information obtained from other states with similar dynamics. Additionally, we propose an algorithm for synthesizing the optimal control strategy in unknown environments by using side information to effectively balance between exploration and exploitation. We prove that, with high probability, the proposed algorithm yields a near-optimal policy in the Bayesian sense, while also guaranteeing the safety of the agent during exploration. We obtain the near-optimal policy in time steps that are polynomial in terms of the parameters describing the model. We illustrate the utility of the proposed algorithms in a setting of a Mars rover, with data from onboard sensors and a companion aerial vehicle acting as the side information.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • Nondisturbing Extremum Seeking Control for Multiagent Industrial Systems

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      Authors: Mark Haring;Synne Fossøy;Thiago Lima Silva;Alexey Pavlov;
      Pages: 1492 - 1507
      Abstract: Industrial applications of extremum seeking control (ESC) can be a hit and miss affair. Although a gain in performance can be achieved, the dither applied to excite the system causes unwanted fluctuations in the performance of the system. The fluctuations in systems with a single extremum seeking loop are generally small. However, for systems with many extremum seeking loops, the fluctuations in each loop may add up to an intolerable amount of fluctuation in the total performance. In this article, we propose a method to cancel the dither-induced fluctuations in the overall system performance to a large extent by smartly constructing the dither signals in each extremum seeking loop using a centralized coordinator. The novelty of our method lies in the direct calculation of the dither signals that avoids the heavy computations required by other methods. Moreover, we provide a solvability analysis for the problem of cancelling dither-induced fluctuations in the total performance of the system. Furthermore, a complete stability analysis of the overall ESC scheme with dither coordination is given.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • On Semi-Global Exponential Stability Under Sampling for Locally Lipschitz
           Time-Delay Systems

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      Authors: Mario Di Ferdinando;Pierdomenico Pepe;Stefano Di Gennaro;
      Pages: 1508 - 1523
      Abstract: In this article, the stability preservation problem for locally Lipschitz nonlinear time-delay systems, by emulation of continuous-time dynamic output feedback controllers, is studied. Sufficient Lyapunov-like conditions are provided such that, by suitably fast sampling, the Euler emulation of continuous-time dynamic output feedback controllers ensures the semiglobal exponential stability of the related sampled-data closed-loop system. If other emulation schemes than the Euler one are used, practical semiglobal exponential stability is guaranteed, with arbitrarily small final target ball, by suitably fast sampling. The intersampling system behavior, as well as time-varying sampling intervals, is taken into account. In the provided results, the delay-free case is included as a special case. The practical applicability of the provided results is shown through the study of a nonlinear chemical reactor system with recycle and a glucose–insulin system.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • Nonlinear Stochastic Model Predictive Control: Existence, Measurability,
           and Stochastic Asymptotic Stability

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      Authors: Robert D. McAllister;James B. Rawlings;
      Pages: 1524 - 1536
      Abstract: In this article, we establish a collection of new theoretical properties for nonlinear stochastic model predictive control (SMPC). Based on the concept of stochastic input-to-state stability (SISS), we define robust asymptotic stability in expectation (RASiE) and establish that nonlinear SMPC renders the origin of the closed-loop system RASiE. Moreover, we establish several new foundational results that have not been addressed in previous research. Specifically, we verify that, under basic regularity assumptions, a solution to the SMPC optimization problem exists and the closed-loop trajectory is Borel measurable thereby guaranteeing that all relevant stochastic properties of the closed-loop system are indeed well-defined. We present a numerical example to demonstrate the nonintuitive behavior that can arise from nonlinear SMPC.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • Adversarial Resilience for Sampled-Data Systems Under High-Relative-Degree
           Safety Constraints

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      Authors: James Usevitch;Dimitra Panagou;
      Pages: 1537 - 1552
      Abstract: Control barrier functions (CBFs) have recently become a powerful method for rendering desired safe sets forward invariant in single-agent and multiagent systems. In the multiagent case, prior literature has considered scenarios where all agents cooperate to ensure that the corresponding set remains invariant. However, these works do not consider scenarios where a subset of the agents are behaving adversarially with the intent to violate safety bounds. In addition, prior results on multiagent CBFs typically assume that control inputs are continuous and do not consider sampled-data dynamics. This article presents a framework for normally behaving agents in a multiagent system with heterogeneous control-affine, sampled-data dynamics to render a safe set forward invariant in the presence of adversarial agents. The proposed approach considers several aspects of practical control systems including input constraints, clock asynchrony and disturbances, and distributed calculation of control inputs. Our approach also considers functions describing safe sets having high relative degree with respect to system dynamics. The efficacy of these results are demonstrated through simulations.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • Robustness of Learning in Games With Heterogeneous Players

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      Authors: Aqsa Shehzadi Akbar;Hassan Jaleel;Waseem Abbas;Jeff S. Shamma;
      Pages: 1553 - 1567
      Abstract: We consider stochastic learning dynamics in games and present a novel notion of robustness to heterogeneous players for a stochastically stable action profile. A standard assumption in these dynamics is that all the players are homogeneous, and their decision strategies can be modeled as perturbed versions of myopic best or better response strategies. We relax this assumption and propose a robustness criteria, which characterizes a stochastically stable action profile as robust to heterogeneous behaviors if a small fraction of heterogeneous players cannot alter the long-run behavior of the rest of the population. In particular, we consider confused players who randomly update their actions, stubborn players who never update their actions, and strategic players who attempt to manipulate the population behavior. We establish that radius–coradius based analysis can provide valuable insights into the robustness properties of stochastic learning dynamics for various game settings. We derive sufficient conditions for a stochastically stable profile to be robust to a confused, stubborn, or strategic player and elaborate these conditions through carefully designed examples. Then we explore the role of network structure in our proposed notion of robustness by considering graphical coordination games and identifying network topologies in which a single heterogeneous player is sufficient to alter the population’s behavior. Our results will provide foundations for future research on designing networked systems that are robust to players with heterogeneous decision strategies.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • Event-Triggered Adaptive Control of Coupled Hyperbolic PDEs With
           Piecewise-Constant Inputs and Identification

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      Authors: Ji Wang;Miroslav Krstic;
      Pages: 1568 - 1583
      Abstract: In this article, we present an event-triggered boundary control scheme with a, likewise, event-triggered batch least-squares parameter identification for a $2times 2$ hyperbolic PDE-ODE system, where two coefficients of the in-domain couplings between two transport PDEs, and the system parameter of a scalar ODE, are unknown. The triggering condition is designed based on evaluating both the actuation deviation caused by the difference between the plant states and their sampled values, and the growth of the plant norm. When either condition is met, the piecewise-constant control input and parameter estimates are updated simultaneously. For the closed-loop system, the following results are proved: first, the absence of a Zeno phenomenon; second, finite-time exact identification of the unknown parameters in most situations; third, exponential regulation of the plant states to zero. In the numerical simulation, the design is verified in an application of axial vibration control of a mining cable elevator, where the damping coefficients of the cable and the cage are unknown.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • Global Asymptotic Tracking for Marine Vehicles Using Adaptive Hybrid
           Feedback

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      Authors: Erlend A. Basso;Henrik M. Schmidt-Didlaukies;Kristin Y. Pettersen;Asgeir J. Sørensen;
      Pages: 1584 - 1599
      Abstract: This article presents an adaptive hybrid feedback control law for global asymptotic tracking of a hybrid reference system for marine vehicles in the presence of parametric modeling errors. The reference system is constructed from a parameterized loop and a speed assignment specifying the motion along the path, which decouples the geometry of the path from the motion along the path. During flows, the hybrid feedback consists of a proportional-derivative action and an adaptive feedforward term, while a hysteretic switching mechanism that is independent of the vehicle velocities determines jumps. The effectiveness of the proposed control law is demonstrated through experiments.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • Enhanced P-Type Control: Indirect Adaptive Learning From Set-Point Updates

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      Authors: Ronghu Chi;Huaying Li;Dong Shen;Zhongsheng Hou;Biao Huang;
      Pages: 1600 - 1613
      Abstract: In this article, an indirect adaptive iterative learning control (iAILC) scheme is proposed for both linear and nonlinear systems to enhance the P-type controller by learning from set points. An adaptive mechanism is included in the iAILC method to regulate the learning gain using input–output measurements in real time. An iAILC method is first designed for linear systems to improve control performance by fully utilizing model information if such a linear model is known exactly. Then, an iterative dynamic linearization (IDL)-based iAILC is proposed for a nonlinear nonaffine system, whose model is completely unknown. The IDL technique is employed to deal with the strong nonlinearity and nonaffine structure of the systems such that a linear data model can be attained consequently for the algorithm design and performance analysis. The convergence of the developed iAILC schemes is proved rigorously, where contraction mapping, two-dimensional (2-D) Roesser’s system theory, and mathematical induction are employed as the basic analysis tools. Simulation studies are provided to verify the developed theoretical results.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • Assign and Appraise: Achieving Optimal Performance in Collaborative Teams

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      Authors: Elizabeth Y. Huang;Dario Paccagnan;Wenjun Mei;Francesco Bullo;
      Pages: 1614 - 1627
      Abstract: Tackling complex team problems requires understanding each team member’s skills in order to devise a task assignment maximizing the team performance. This article proposes a novel quantitative model describing the decentralized process by which individuals in a team learn who has what abilities, while concurrently assigning tasks to each of the team members. In the model, the appraisal network represents team members’ evaluations of one another, and each team member chooses their own workload. The appraisals and workload assignment change simultaneously: each member builds their own local appraisal of neighboring members based on the performance exhibited on previous tasks, while the workload is redistributed based on the current appraisal estimates. We show that the appraisal states can be reduced to a lower dimension due to the presence of conserved quantities associated with the cycles of the appraisal network. Building on this, we provide rigorous results characterizing the ability, or inability, of the team to learn each other’s skills and, thus, converge to an allocation maximizing the team performance. We complement our analysis with extensive numerical experiments.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • Nonasymptotic Bounds for Stochastic Optimization With Biased Noisy
           Gradient Oracles

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      Authors: Nirav Bhavsar;L. A. Prashanth;
      Pages: 1628 - 1641
      Abstract: We introduce biased gradient oracles to capture a setting where the function measurements have an estimation error that can be controlled through a batch size parameter. Our proposed oracles are appealing in several practical contexts, for instance, risk measure estimation from a batch of independent and identically distributed samples, or simulation optimization, where the function measurements are “biased” due to computational constraints. In either case, increasing the batch size reduces the estimation error. We highlight the applicability of our biased gradient oracles in a risk-sensitive reinforcement learning setting. In the stochastic nonconvex optimization context, we analyze a variant of the randomized stochastic gradient algorithm with a biased gradient oracle. We quantify the convergence rate of this algorithm by deriving nonasymptotic bounds on its performance. Next, in the stochastic convex optimization setting, we derive nonasymptotic bounds for the last iterate of a stochastic gradient descent algorithm with a biased gradient oracle.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • Distributed Filtering Under Constrained Bit Rate Over Wireless Sensor
           Networks: Dealing With Bit Rate Allocation Protocol

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      Authors: Jun-Yi Li;Zidong Wang;Renquan Lu;Yong Xu;
      Pages: 1642 - 1654
      Abstract: This article is concerned with the distributed filtering issue for linear discrete-time systems under bounded noises and constrained bit rate over wireless sensor networks. The communication between different sensor nodes is implemented via a wireless digital communication network with limited bandwidth. A bit rate constraint, which is subject to the so-called bandwidth allocation strategy, is placed to quantify the effect of the network bandwidth on the distributed filtering performance. An improved coding–decoding procedure is proposed to enable each node to decode messages from its neighbor nodes. Based on this procedure, a decoded-innovation-based distributed filtering scheme is put forward and a sufficient condition is established to ensure that the filtering error dynamics is ultimately bounded. Subsequently, a relationship between the bit rate and certain specific filtering performance is discovered. The desired parameters of the distributed filter are determined via solving two optimization problems whose objectives are actually the filtering performance indices including the smallest ultimate bound and the fastest decay rate. Furthermore, the codesign issue of the bit rate allocation protocol and the filter gain is converted into the mixed integer nonlinear programming problem, which is solved by means of the particle swarm optimization algorithm and the linear matrix inequality technique. Finally, numerical simulations on three scenarios are provided to verify the validity of the proposed distributed filtering approach.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • Zonotope-Based Interval Estimation for 2-D FMLSS Systems Using an
           Event-Triggered Mechanism

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      Authors: Liu Yang;Zhongyang Fei;Zhenhua Wang;Choon Ki Ahn;
      Pages: 1655 - 1666
      Abstract: This study considers the problem of interval estimation for event-triggered 2-D systems in Fornasini–Marchesini local state-space (FMLSS) model. In such systems, the general hypothesis for practical applications is that exogenous disturbances and measurement noise are unknown but bounded. First, an observer design criterion is deduced from the defined zonotopic radius and weighted radius of the FMLSS model. Subsequently, to economize the restricted communication resources, an event-driven data transmission strategy is employed for the observer. Then, a sufficient criterion is established to guarantee the $mathcal {L}_infty$ performance for the considered 2-D FMLSS system with the event-triggered scheme. From the obtained observer, the estimation interval of the 2-D system is derived by using the zonotopic state estimation approach. Finally, the validity is confirmed by simulations.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • Identifiability in the Behavioral Setting

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      Authors: Ivan Markovsky;Florian Dörfler;
      Pages: 1667 - 1677
      Abstract: Identifiability, i.e., uniqueness of a solution of the identification problem, is a fundamental issue in system identification and data-driven control. Necessary and sufficient identifiability conditions for deterministic linear time-invariant systems that do not require a priori given input/output partitioning of the variables nor controllability of the true system are derived in the article. The prior knowledge needed for identifiability is the number of inputs, lag, and order of the true system. Our results are based on a modification of the notion of a most powerful unfalsified model for finite data and a novel algorithm for its computation. We provide a generalization of a result that became known as the fundamental lemma and a novel nonparametric data-driven representation of the system behavior based on general data matrix structures. The results assume exact data, however, low-rank approximation allows their application in the case of noisy data. We compare empirically low-rank approximation of the Hankel, Page, and trajectory matrices in the errors-in-variables setting. Although the Page and trajectory matrices are unstructured, the parameter estimates obtained are less accurate than the one obtained from the Hankel matrix.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • Transition-Dependent Bumpless Transfer Control Synthesis of Switched
           Linear Systems

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      Authors: Lixian Zhang;Kaixin Xu;Jianan Yang;Minghao Han;Shuai Yuan;
      Pages: 1678 - 1684
      Abstract: This technical article is concerned with the bumpless transfer control synthesis of a class of discrete-time switched linear systems with general persistent dwell-time (PDT) switching signals. A novel piecewise transition-dependent controller facilitating the control synthesis is designed, which ensures the stability of the closed-loop system as well as the bumpless transfer at the occurrence of the switching. By imposing the given bumps limitation constraints, sufficient conditions on the existence of the controller are developed for the nominal systems. The results are extended to the case of the disturbed systems with an ensured $ell _{2}$-gain. Numerical examples are provided to demonstrate the effectiveness and potential of the theoretical results.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • Finite-Time Stabilization and Optimal Feedback Control for Nonlinear
           Discrete-Time Systems

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      Authors: Wassim M. Haddad;Junsoo Lee;
      Pages: 1685 - 1691
      Abstract: Finite-time stability involves dynamical systems whose trajectories converge to an equilibrium state in finite time. Sufficient conditions for finite-time stability have recently been developed in the literature for discrete-time dynamical systems. In this article, we build on these results to develop a framework for addressing the problem of optimal nonlinear analysis and feedback control for finite-time stability and finite-time stabilization for nonlinear discrete-time controlled dynamical systems. Finite-time stability of the closed-loop nonlinear system is guaranteed by means of a Lyapunov function that satisfies a difference inequality involving fractional powers and a minimum operator. This Lyapunov function can clearly be seen to be the solution to a difference equation that corresponds to a steady-state form of the Bellman equation, and hence, guaranteeing both finite-time stability and optimality. Finally, a numerical example is presented to demonstrate the efficacy of the proposed finite-time discrete stabilization framework.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • Decentralized Backstepping Control for Interconnected Systems With
           Non-Triangular Structural Uncertainties

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      Authors: Jianping Cai;Changyun Wen;Lantao Xing;Qiuzhen Yan;
      Pages: 1692 - 1699
      Abstract: In this article, a decentralized adaptive control scheme is proposed based on backstepping techniques for a class of uncertain interconnected systems with unknown modeling errors and interactions. Unlike existing results, the functions bounding modeling errors and interactions are not required to meet the triangular condition. Namely, such functions are allowed to depend on all the states of the interconnected system. It is shown that the proposed adaptive control scheme can ensure all signals in the closed-loop system bounded. Simulation studies verify the effectiveness of the proposed scheme.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • Adaptive Constrained Formation-Tracking Control for a Tractor-Trailer
           Mobile Robot Team With Multiple Constraints

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      Authors: Xu Jin;Shi-Lu Dai;Jianjun Liang;
      Pages: 1700 - 1707
      Abstract: A team of multiple tractor–trailer mobile robots has many applications in areas including agriculture, logistics, transportation, etc. In this article, we propose a novel adaptive constrained formation-tracking control algorithm for the trailers in a tractor–trailer mobile robot team to track a desired formation, while satisfying multiple precision, safety, and feasibility constraint requirements during the operation. Both universal barrier function approaches and a novel state transformation scheme are incorporated to deal with constraints of different nature. Adaptive estimators are introduced to estimate the rate of change of the desired trajectories for all trailers. We show that exponential convergence to a small neighborhood of the equilibrium can be guaranteed. In the end, a MATLAB simulation example and a Gazebo simulator study further demonstrate the efficacy of the proposed algorithm.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • On the Semidecidability of the Remote State Estimation Problem

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      Authors: Holger Boche;Yannik N. Böck;Christian Deppe;
      Pages: 1708 - 1714
      Abstract: In this article, we consider the decision problem associated with the task of remotely estimating the state of a dynamic plant via a noisy communication channel. Given a machine-readable description of the plant’s and channel’s characteristics, does there exist an algorithm that decides whether remote state estimation is possible' From an analytic point of view, this problem has been shown to involve the zero-error capacity of the communication channel. By applying results from Turing machine theory and zero-error coding, we analyze several related variants of the decision problem mentioned above. Our analysis also incorporates a weakened form of the state estimation objective, which has been shown to depend on the classical Shannon Capacity instead. In the broadest sense, our results yield a fundamental limit to the capabilities of computer-aided design tools and adaptive autonomous systems, assuming they are based on digital hardware.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • Necessary and Sufficient Conditions for Difference Flatness

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      Authors: Bernd Kolar;Johannes Diwold;Markus Schöberl;
      Pages: 1715 - 1721
      Abstract: In this article, we show that the flatness of a nonlinear discrete-time system can be checked by computing a unique sequence of involutive distributions. The well-known test for static feedback linearizability is included as a special case. Since the computation of the sequence of distributions requires only the solution of algebraic equations, it allows an efficient implementation in a computer algebra program. In case of a positive result, a flat output can be obtained by straightening out the involutive distributions with the Frobenius theorem.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • Event-Triggered Fixed-Time Stabilization of Two Time Scales Linear Systems

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      Authors: Yan Lei;Yan-Wu Wang;Irinel-Constantin Morărescu;Romain Postoyan;
      Pages: 1722 - 1729
      Abstract: This article investigates the fixed-time stabilization of uncertain linear time-invariant systems exhibiting two time scales using a state-feedback event-triggered controller. We proceed by emulation and assume that we know a controller that solves the problem in the absence of sampling. We then take sampling into account and present an event-triggered strategy, which consists of two independent sampling mechanisms, associated with the slow and the fast subsystems, respectively. In this setting, the fixed-time stability property becomes practical, where the adjustable parameters are constants used to define the triggering rules. The existence of a strictly positive time between any two successive transmissions is ensured for each transmission law. A numerical example is provided to illustrate the effectiveness of the results.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • Distributed Interval Consensus of Multiagent Systems With a Pulse Width
           Modulation Protocol

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      Authors: Yao Zou;Kewei Xia;Zongyu Zuo;Zhengtao Ding;
      Pages: 1730 - 1737
      Abstract: Rather than the conventional pure consensus, this article proposes a pulsewidth modulation (PWM) protocol for an improved interval consensus of multiagent systems from the distributed perspective. In particular, the consensus of all the agents is achieved within a prescribed interval, which is merely available to partial agents, though. The PWM protocol effectively relieves the running burden of the agents without the additional analogue-to-digital conversion. The modulation periods of different agents are allowed to be heterogeneous. By nominating the agents that have access to the given interval as leaders and others as followers, the proposed distributed PWM protocol introduces a projection operator for the leaders in order for the collective aggregation. In terms of the graph connectivity condition of containing a spanning tree, it is shown that the concerned interval consensus objective is realized by the proposed distributed PWM protocol. Simulations are eventually performed to validate the established theoretical results.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • Projection Scheme and Adaptive Control for Symmetric Matrices With
           Eigenvalue Bounds

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      Authors: Rahul Moghe;Maruthi R. Akella;
      Pages: 1738 - 1745
      Abstract: A projection scheme to handle eigenvalue bounds for adaptive control with uncertain symmetric matrix parameters is introduced. Conventional parameter projection techniques are generally unable to handle explicit eigenvalue bounds. The continuous projection scheme presented here maintains the closed-loop stability properties for adaptive controllers while simultaneously satisfying a priori available eigenvalue bounds of the uncertain symmetric matrix valued parameters. The projection scheme uses the eigen decomposition of the symmetric matrix parameter to project its eigenvalues to lie within the prescribed bounds. The eigenvalues of the symmetric matrix may be lower bounded or upper bounded or both. A direct adaptation over the eigenvalues and the eigen projections of the symmetric matrix parameter is also derived to help circumvent expensive eigen decomposition calculations. The new projection here shows improved performance in numerical simulations of rigid body attitude tracking control and trajectory tracking of robotic manipulators with unknown inertia parameters.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • Reinforcement Learning of Structured Stabilizing Control for Linear
           Systems With Unknown State Matrix

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      Authors: Sayak Mukherjee;Thanh Long Vu;
      Pages: 1746 - 1752
      Abstract: This article delves into designing stabilizing feedback control gains for continuous-time linear systems with unknown state matrix, in which the control gain is subjected to a structural constraint. We bring forth the ideas from reinforcement learning (RL) in conjunction with sufficient stability and performance guarantees in order to design these structured gains using the trajectory measurements of states and controls. We first formulate a model-based linear quadratic regulator (LQR) framework to compute the structured control gain. Subsequently, we transform this model-based LQR formulation into a data-driven RL algorithm to remove the need for knowing the system state matrix. Theoretical guarantees are provided for the stability of the closed-loop system and the convergence of the structured RL (SRL) algorithm. A remarkable application of the proposed SRL framework is in designing distributed static feedback control, which is necessary for automatic control of many large-scale cyber–physical systems. As such, we validate our theoretical results with numerical simulations on a multiagent networked linear time-invariant dynamical system.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • A Linearly Convergent Distributed Nash Equilibrium Seeking Algorithm for
           Aggregative Games

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      Authors: Shijie Huang;Jinlong Lei;Yiguang Hong;
      Pages: 1753 - 1759
      Abstract: This article considers distributed Nash equilibrium (NE) seeking of strongly monotone aggregative games over a multiagent network. Each player can only observe its own strategy while can exchange information with its neighbors via a communication graph. To solve the problem, we propose a distributed algorithm with multiple rounds of communication, where the players need constant rounds of communication with their neighbors at each iteration. We then prove that our algorithm converges to the (unique) NE with a linear convergence rate. We further study a single-round communication version of our algorithm, which can also achieve linear convergence rate with an additional condition related to the structure of the graph and the properties of the aggregative game. Finally, we provide numerical simulations to verify our results.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • Dynamical Primal-Dual Nesterov Accelerated Method and Its Application to
           Network Optimization

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      Authors: Xianlin Zeng;Jinlong Lei;Jie Chen;
      Pages: 1760 - 1767
      Abstract: This article develops a continuous-time primal-dual accelerated method with an increasing damping coefficient for a class of convex optimization problems with affine equality constraints. This article analyzes critical values for parameters in the proposed method and prove that the rate of convergence in terms of the duality gap function is $O(frac{1}{t^2})$ by choosing suitable parameters. As far as we know, this is the first continuous-time primal-dual accelerated method that can obtain the optimal rate. Then, this article applies the proposed method to two network optimization problems, a distributed optimization problem with consensus constraints and a distributed extended monotropic optimization problem, and obtains two variant distributed algorithms. Finally, numerical simulations are given to demonstrate the efficacy of the proposed method.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • Computation of the Distance-Based Bound on Strong Structural
           Controllability in Networks

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      Authors: Mudassir Shabbir;Waseem Abbas;A. Yasin Yazıcıoğlu;Xenofon Koutsoukos;
      Pages: 1768 - 1775
      Abstract: In this article, we study the problem of computing a tight lower bound on the dimension of the strong structurally controllable subspace (SSCS) in networks with Laplacian dynamics. The bound is based on a sequence of vectors containing the distances between leaders (nodes with external inputs) and followers (remaining nodes) in the underlying network graph. Such vectors are referred to as the distance-to-leaders vectors. We give exact and approximate algorithms to compute the longest sequences of distance-to-leaders vectors, which directly provide distance-based bounds on the dimension of SSCS. The distance-based bound is known to outperform the other known bounds (for instance, based on zero-forcing sets), especially when the network is partially strong structurally controllable. Using these results, we discuss an application of the distance-based bound in solving the leader selection problem for strong structural controllability. Further, we characterize strong structural controllability in path and cycle graphs with a given set of leader nodes using sequences of distance-to-leaders vectors. Finally, we numerically evaluate our results on various graphs.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • Self-Triggered Stabilization of Discrete-Time Linear Systems With
           Quantized State Measurements

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      Authors: Masashi Wakaiki;
      Pages: 1776 - 1783
      Abstract: We study the self-triggered stabilization of discrete-time linear systems with quantized state measurements. In the networked control system we consider, sensors may be spatially distributed and be connected to a self-triggering mechanism through finite data-rate channels. Each sensor independently encodes its measurements and sends them to the self-triggering mechanism. The self-triggering mechanism integrates quantized measurement data and then computes sampling times. Assuming that the closed-loop system is stable in the absence of quantization and self-triggered sampling, we propose a joint design method of an encoding scheme and a self-triggering mechanism for stabilization. To deal with data inaccuracy due to quantization, the proposed self-triggering mechanism uses not only quantized data but also an upper bound of quantization errors, which is shared with a decoder.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • Impulsive Stabilization of Systems With Control Delay

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      Authors: Lirong Huang;Sheng Xu;
      Pages: 1784 - 1791
      Abstract: Input delay could lead to instability of an impulsive control system. However, the problem of input delay has been seldom studied. This work addresses the fundamentally important problem in impulsive control theory. By a novel approach, we establish Lyapunov–Razumikhin-type theorems on exponential stability of stochastic impulsive delay systems. Based on our fundamental results, we develop a foundational theory for the impulsive stabilization of stochastic delay systems with input delay. Our proposed theory provides an impulsive control design method for systems with input delay and the upper bound of input delay which the designed impulsive control scheme can admit while the resulting controlled system remains exponentially stable. As future work, we present event-triggered impulsive control systems that are a natural and significant generalization of the classical impulsive control systems in the literature.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • Measurement Difference Method: A Universal Tool for Noise Identification

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      Authors: Oliver Kost;Jindřich Duník;Ondřej Straka;
      Pages: 1792 - 1799
      Abstract: This article deals with noise identification of a system described by the linear time-varying state-space model using correlation methods. In particular, the stress is laid on the measurement difference method (MDM) as a universal tool allowing estimation of moments and parameters of the state and measurement noises. The recent results are summarized in a common framework and the full (and weighted) MDM implementation is developed. This implementation provides unbiased and weakly consistent estimate of an arbitrary raw or central moment of the state and measurement noises. The performance of the method is shown in a numerical study.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • Linear-Quadratic Non-Zero Sum Backward Stochastic Differential Game With
           Overlapping Information

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      Authors: Shuang Wu;
      Pages: 1800 - 1806
      Abstract: This article studies a linear quadratic non-zero sum stochastic differential game with overlapping information, where the state dynamics are described by a backward stochastic differential equation and the information obtained by two players has a common part but no inclusion relation. The open-loop Nash equilibrium strategy is given by some conditional mean-field stochastic differential equations. In addition, coupled Riccati equations are introduced to express the state feedback form of the Nash equilibrium strategy.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • Frequency Response Function-Based Learning Control: Analysis and Design
           for Finite-Time Convergence

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      Authors: Robin de Rozario;Tom Oomen;
      Pages: 1807 - 1814
      Abstract: Iterative learning control (ILC) enables substantial performance improvement by using past operational data in combination with approximate plant models. The aim of this article is to develop an ILC framework based on nonparametric frequency response function (FRF) models that requires very limited modeling effort. These FRF models describe the behavior of a system in periodic steady state, yet are employed for the control of arbitrary finite-length tasks. A detailed analysis and design framework is developed to construct noncausal learning filters directly from uncertain FRF models, that achieve ILC convergence for arbitrary tasks. The resulting framework provides a unification between ILC and iterative inversion-based control, where the latter is a learning method specifically developed for periodic tasks.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • State Distribution of Markovian Jump Boolean Networks and Its Applications

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      Authors: Min Meng;Gaoxi Xiao;
      Pages: 1815 - 1822
      Abstract: This article investigates the state distribution of Markovian jump Boolean networks subject to stochastic disturbances based on the measured outputs. The considered disturbances are modeled as independent and identically distributed processes with known probability distributions. An iterative algorithm is proposed to compute conditional probability distributions of the current state and one-step predicted state based on the knowledge of the output measurements. The obtained conditional probability distributions can be applied to study the optimal state estimation, reconstructibility, and fault detection of Markovian jump Boolean networks.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • Transient Growth of Accelerated Optimization Algorithms

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      Authors: Hesameddin Mohammadi;Samantha Samuelson;Mihailo R. Jovanović;
      Pages: 1823 - 1830
      Abstract: Optimization algorithms are increasingly being used in applications with limited time budgets. In many real-time and embedded scenarios, only a few iterations can be performed and traditional convergence metrics cannot be used to evaluate performance in these nonasymptotic regimes. In this article, we examine the transient behavior of accelerated first-order optimization algorithms. For convex quadratic problems, we employ tools from linear systems theory to show that transient growth arises from the presence of nonnormal dynamics. We identify the existence of modes that yield an algebraic growth in early iterations and quantify the transient excursion from the optimal solution caused by these modes. For strongly convex smooth optimization problems, we utilize the theory of integral quadratic constraints to establish an upper bound on the magnitude of the transient response of Nesterov’s accelerated algorithm. We show that both the Euclidean distance between the optimization variable and the global minimizer and the rise time to the transient peak are proportional to the square root of the condition number of the problem. Finally, for problems with large condition numbers, we demonstrate tightness of the bounds that we derive up to constant factors.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • Argument Principle and Integral Relations: Hidden Links and Generalized
           Forms

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      Authors: Yong Xu;Gang Chen;Jie Chen;Li Qiu;
      Pages: 1831 - 1838
      Abstract: This article studies Bode/Poisson type sensitivity and complementary sensitivity integral relations for linear, time-invariant feedback control systems. We call for attention to a seemingly unnoticed link between the well-known argument principle and Bode/Poisson integral relations. In this respect, a generalized version of the argument principle is developed, under which it is shown that various Bode/Poisson type integral relations can be unified, and new integral relations can be derived. The new and classical integral relations together provide useful insights for analyzing control design limitations and tradeoffs.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • Predictor-Based Global Sampled-Data Output Feedback Stabilization for
           Nonlinear Uncertain Systems Subject to Delayed Output

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      Authors: Jiankun Sun;Jun Yang;Shihua Li;Zhigang Zeng;
      Pages: 1839 - 1846
      Abstract: In this article, we address the problem of global sampled-data output feedback stabilization for a class of nonlinear uncertain systems with delayed output using the continuous-discrete method. Thanks to the prediction technique and feedback domination method, a novel coupled design method of predictor-based continuous-discrete observer and linear controller is proposed when only delayed sampled-data output is accessible. The proposed predictor-based observer can effectively estimate the unknown state by compensating the influences of sampling and output delay. The main advantage of the proposed control method is that the full state information and accurate model nonlinearities do not need to be known any more. The global exponential stability of the overall hybrid control system can be ensured when there hold some sufficient conditions with respect to the maximum allowable sampling period and output delay.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • Distributed Optimization With Coupling Constraints

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      Authors: Xuyang Wu;He Wang;Jie Lu;
      Pages: 1847 - 1854
      Abstract: In this article, we investigate distributed convex optimization with both inequality and equality constraints, where the objective function can be a general nonsmooth convex function and all the constraints can be both sparsely and densely coupling. By strategically integrating ideas from primal-dual, proximal, and virtual-queue optimization methods, we develop a novel distributed algorithm, referred to as IPLUX, to address the problem over a connected, undirected graph. We show that IPLUX achieves an $O(1/k)$ rate of convergence in terms of optimality and feasibility, which is stronger than the convergence results of the alternative methods and eliminates the standard assumption on the compactness of the feasible region. Finally, IPLUX exhibits faster convergence and higher efficiency than several state-of-the-art methods in the simulation.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • SMC for Discrete-Time Nonlinear Semi-Markovian Switching Systems With
           Partly Unknown Semi-Markov Kernel

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      Authors: Wenhai Qi;Guangdeng Zong;Yakun Hou;Mohammed Chadli;
      Pages: 1855 - 1861
      Abstract: This article is devoted to the discrete-time sliding mode control (DSMC) for nonlinear semi-Markovian switching systems (S-MSSs). Motivated by the fact that the complete information of the semi-Markov Kernel is difficult to be obtained in practical applications, it is recognized to be partly unknown as the most common mean. By utilizing the prior information of the sojourn-time upper bound for each switching mode, sufficient conditions under the equivalent DSMC law are proposed for the mean square stability. Moreover, the designed DSMC law realizes the finite-time reachability of the sliding region, and makes the sliding dynamics converge to the predesignated sliding region in a finite time. In the end, a numerical example and an electronic throttle model are given to validate the proposed control strategy.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • Hyperexponential and Fixed-Time Stability of Time-Delay Systems:
           Lyapunov–Razumikhin Method

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      Authors: Artem N. Nekhoroshikh;Denis Efimov;Andrey Polyakov;Wilfrid Perruquetti;Igor B. Furtat;
      Pages: 1862 - 1869
      Abstract: Razumikhin-like theorems on hyperexponential and fixed-time stability of time-delay systems are proposed for both explicitly and implicitly defined Lyapunov functions. While the former method is useful for stability analysis, the latter approach is more suitable for control synthesis. Examples of systems that can be stabilized hyperexponentially and in fixed time are given. The control parameters tuning algorithm is presented in the form of linear matrix inequalities. The numerical simulations illustrate the theoretical results.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • Using Memoryless Output Feedback to Semiglobally Stabilize a Class of SISO
           Nonlinear Systems With Input Delay

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      Authors: Wei Lin;Jiwei Sun;
      Pages: 1870 - 1877
      Abstract: Using the semiglobal control idea from the recent work (Wang and Lin, 2022), we present a different semiglobal method without invoking the dynamic extension technique to prove that in the single-input-single-output (SISO) case, certain nonaffine systems with input delay are semiglobally asymptotically stabilizable (SGAS) via $n$-dimensional memoryless output feedback. A dynamic output compensator is constructed and composed of an $n$-dimensional nonlinear observer and an observer-based controller, both of them with saturated states. As a consequence, an affine system in a lower triangular form with input delay is shown to be SGAS by $n$-dimensional memoryless output feedback. These results provide some answers to the open question in (Wang and Lin, 2022) where $(2n-1)$-dimensional output feedback controllers were found by a dynamic extension method: when and how to design delay-free, $n$-dimensional SGAS output feedback controllers for a significant class of SISO nonaffine systems'
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • Event-Triggered Model Reference Adaptive Control for Linear Partially
           Time-Variant Continuous-Time Systems With Nonlinear Parametric Uncertainty
           

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      Authors: Yi Jiang;Dawei Shi;Jialu Fan;Tianyou Chai;Tongwen Chen;
      Pages: 1878 - 1885
      Abstract: In this work, we develop an event-triggered adaptive control approach for solving the state tracking problem of linear partially time-variant continuous-time systems with the nonlinear state-dependent matched parametric uncertainty under unknown system dynamics. First, an event-triggered model reference adaptive controller is designed, which is composed of event-triggered adaptive laws based on the event-updated information and an event-triggering condition depending on the state tracking error of the controlled plant and reference model. Then, the state-tracking error and the error between control parameters and ideal ones of the resulting closed-loop system are proven to be uniformly ultimately bounded. Moreover, based on the designed event-triggering condition, the interevent time between two consecutive triggering points is proven to have a positive lower bound. Finally, a simulation example is provided to show the effectiveness of the proposed approach.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • Distributed Pinning Set Stabilization of Large-Scale Boolean Networks

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      Authors: Shiyong Zhu;Jianquan Lu;Liangjie Sun;Jinde Cao;
      Pages: 1886 - 1893
      Abstract: In this article, we design the distributed pinning controllers to globally stabilize a Boolean network (BN), especially a sparsely connected large-scale one, toward a preassigned subset of states through the node-to-node message exchange. Given an appointed set of states, system nodes are partitioned into two disjoint parts, whose states are, respectively, fixed or arbitrary with respect to the given state set. With such node division, three parts of pinned nodes are selected and the state feedback controllers are accordingly designed such that the resulting BN satisfies all three conditions: the information of the arbitrary-state nodes cannot be passed to the others, the subgraph of network structure induced by the fixed-state nodes is acyclic, and the fixed states of these nodes are compatible with the preassigned state set. If the network structure of controlling BN is acyclic, the stabilizing time is revealed to be no more than the diameter of the resulting subgraph plus one. Based on this, we further design the pinning controllers with the constraint of stabilizing time. Noting that the overall procedure runs in an exponentially increasing time complexity with respect to the largest number of functional variables in the dynamics of pinned nodes, the sparsely connected large-scale BNs can be well addressed in a reasonable amount of time. Finally, we demonstrate the applications of our theoretical results in a T-cell large granular lymphocyte (T-LGL) survival signal network with 29 nodes and a T-cell receptor signaling network with 90 nodes.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • Consensus-Based Distributed Optimization Enhanced by Integral Feedback

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      Authors: Xuan Wang;Shaoshuai Mou;Brian D. O. Anderson;
      Pages: 1894 - 1901
      Abstract: Inspired and underpinned by the idea of integral feedback, a distributed constant gain algorithm is proposed for multiagent networks to solve convex optimization problems with local linear constraints. Assuming agent interactions are modeled by an undirected graph, the algorithm is capable of achieving the optimum solution with an exponential convergence rate. Furthermore, inherited from the beneficial integral feedback, the proposed algorithm has attractive requirements on communication bandwidth and good robustness against disturbance. Both analytical proof and numerical simulations are provided to validate the effectiveness of the proposed distributed algorithms in solving constrained optimization problems.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • Incentivizing Collaboration in Heterogeneous Teams via Common-Pool
           Resource Games

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      Authors: Piyush Gupta;Shaunak D. Bopardikar;Vaibhav Srivastava;
      Pages: 1902 - 1909
      Abstract: We consider a team of heterogeneous agents, which is collectively responsible for servicing, and subsequently reviewing a stream of homogeneous tasks. Each agent has an associated mean service time and a mean review time for servicing and reviewing the tasks, respectively. Agents receive a reward based on their service and review admission rates. The team objective is to collaboratively maximize the number of “serviced and reviewed” tasks. We formulate a common-pool resource game, and design utility functions to incentivize collaboration among heterogeneous agents in a decentralized manner. We show the existence of a unique pure Nash equilibrium (PNE), and establish convergence of the best response dynamics to this unique PNE. Finally, we establish an analytic upper bound on three inefficiency measures of the PNE, namely the price of anarchy, the ratio of the total review admission rate, and the ratio of latency.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • Solution Algorithms for the Bounded Acceleration Shortest Path Problem

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      Authors: Stefano Ardizzoni;Luca Consolini;Mattia Laurini;Marco Locatelli;
      Pages: 1910 - 1917
      Abstract: The purpose of this article is to introduce and characterize the bounded acceleration shortest path problem (BASP), a generalization of the shortest path problem (SP). This problem is associated to a graph: nodes represent positions of a mobile vehicle and arcs are associated to preassigned geometric paths that connect these positions. The BASP consists in finding the minimum-time path between two nodes. Differently from the SP, the vehicle has to satisfy bounds on maximum and minimum acceleration and speed, which depend on the vehicle’s position on the currently traveled arc. Even if the BASP is NP-hard in the general case, we present a solution algorithm that achieves polynomial time-complexity under some additional hypotheses on problem data.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • A Progressive Bayesian Filtering Framework for Nonlinear Systems With
           Heavy-Tailed Noises

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      Authors: Jie Zhang;Xusheng Yang;Wen-An Zhang;
      Pages: 1918 - 1925
      Abstract: This article studies the Bayesian filtering problem for nonlinear systems with heavy-tailed noises. Because of the nonlinearity and heavy tail characteristics, the Gaussian distribution or particle sets may fail to express the posterior probability density distribution; thus, the progressive Bayesian filtering framework is proposed. With the filtering framework, the measurement update is divided into several steps, and the intermediate posterior distributions are chosen as the importance proposal distributions to improve the approximation of posterior probability density distributions. Moreover, termination conditions for the progressive measurement update are also proposed to improve the robustness of the progressive Bayesian filter against outliers. Finally, a simulation example is exploited to illustrate the effectiveness and superiority of the proposed filtering framework.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • Design and Dynamics Analysis of a Time-Delay Feedback Controller With
           Distributed Characteristic

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      Authors: Binbin Tao;Min Xiao;Wei Xing Zheng;Ying Zhou;Jie Ding;Guoping Jiang;Xiaoqun Wu;
      Pages: 1926 - 1933
      Abstract: At present, some control strategies have been applied to bifurcation control, but these control strategies are flawed. For instance, some new equilibrium are likely to emerge in the controlled system by the state feedback control and proportional–derivative control. The time-delay feedback control and hybrid control may force the controlled system to have discontinuous stable intervals. In consideration of these defects, this article is the first to design a time-delay feedback control strategy with distributed characteristics. The designed controller may not only remain the invariance of the quantity and position of equilibriums, but also ensure the continuity of stability intervals on the controlled system. It is observed that under the specific control parameters, the designed controller can be weakened to the state feedback controller. The bifurcation threshold of the controlled object can be effectively improved. Finally, numerical simulation results verify the validity of the designed controller and illustrate the variations of dynamic performance for the controlled object. Some comparative experiments are directly rendered to reveal the superiority of the designed control strategy.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • Robust Model Predictive Control Using a Two-Step Triggering Scheme

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      Authors: Li Deng;Zhan Shu;Tongwen Chen;
      Pages: 1934 - 1940
      Abstract: This article is concerned with event-triggered robust model predictive control for linear discrete-time systems with bounded disturbances. A two-step scheme involving a tentative verification of a triggering condition and a delayed triggering with a waiting horizon is proposed to reduce the average triggering rate and fully utilize the nominal optimal control sequence minimizing a quadratic cost function. The triggering condition and the waiting horizon are synthesized based on a prediction model of the plant and a robust positively invariant set associated with it. Under mild conditions, recursive feasibility and closed-loop robust stability are guaranteed. Two examples are used to show the effectiveness and merits of the proposed approach.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • Sequential Detection of Replay Attacks

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      Authors: Arunava Naha;André Teixeira;Anders Ahlén;Subhrakanti Dey;
      Pages: 1941 - 1948
      Abstract: One of the most studied forms of attacks on the cyber-physical systems is the replay attack. The statistical similarities of the replayed signal and the true observations make the replay attack difficult to detect. In this article, we address the problem of replay attack detection by adding watermarking to the control inputs and then perform resilient detection using cumulative sum (CUSUM) test on the joint statistics of the innovation signal and the watermarking signal, whereas existing work considers only the marginal distribution of the innovation signal. We derive the expression of the Kullback–Liebler divergence (KLD) between the two joint distributions before and after the replay attack, which is, asymptotically, inversely proportional to the detection delay. We perform a structural analysis of the derived KLD expression and suggest a technique to improve the KLD for the systems with relative degree greater than one. A scheme to find the optimal watermarking signal variance for a fixed increase in the control cost to maximize the KLD under the CUSUM test is presented. We provide various numerical simulation results to support our theory. The proposed method is also compared with a state-of-the-art method based on the Neyman–Pearson detector, illustrating the smaller detection delay of the proposed sequential detector.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • Comments on “New Results on the Characterization of Strictly Positive
           Real Matrix Transfer Functions”

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      Authors: Mojtaba Hakimi-Moghaddam;Augusto Ferrante;
      Pages: 1949 - 1951
      Abstract: In this note, we address an inconsistency in our recent article (Hakimi-Moghaddam and Ferrante, 2021). In particular, we provide an example showing that Lemma III.1 of that article is not correct. We show how the statement of this lemma can be modified in order to resolve this inconsistency. We also describe how this modification impact the other results of the article. Notwithstanding the fact that Lemma III.1 plays a central role in the proof of the main result of (Hakimi-Moghaddam and Ferrante, 2021), we show that such a result is indeed correct as it may be derived by using the modified (and correct) version of the lemma. An illustrative example is provided to support the results.
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
  • Introducing IEEE Collabratec

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      Pages: 1952 - 1952
      PubDate: March 2023
      Issue No: Vol. 68, No. 3 (2023)
       
 
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