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Automation & Control Systems
Shihao Wang, Shiqi Zheng, Choon Ki Ahn, Peng Shi, Xiaowei Jiang
Summary: This paper investigates the event-triggered cooperative output regulation problem for uncertain multi-agent systems and demonstrates the convergence of the output regulation error. The proposed method deals with unknown parameter systems and allows for heterogeneous follower systems. It is a fully distributed controller that only requires relative outputs and incorporates a reduced-order observer and adaptive event-triggered mechanisms. The approach is validated through simulations and experiments on a multi-joint robot manipulator.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Automation & Control Systems
Jieshuai Wu, Maobin Lu, Fang Deng, Jie Chen
Summary: This article investigates the cooperative robust output regulation problem of multi-agent systems with general linear uncertain dynamics using event-triggered control. In comparison to the practical solution achieved by a distributed event-triggered control law, this study proposes a distributed event-triggered output feedback control law and a class of dynamic event-triggered mechanisms to solve the problem accurately.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Computer Science, Artificial Intelligence
Dong Liang, Yi Dong
Summary: This paper proposes a distributed state and output feedback control law based on the dynamic event-triggered mechanism to solve the robust cooperative output regulation problem for a class of general linear uncertain multi-agent systems (MASs) subject to external disturbances. The proposed control law is directly implementable in the digital platform and can handle parametric uncertainties, reject external disturbances, and track a dynamic leader, while avoiding the Zeno phenomenon.
Article
Automation & Control Systems
Dianqiang Li, Tao Li
Summary: We study the cooperative output feedback tracking control of a stochastic nonlinear heterogeneous leader-following multi-agent system. Each agent has continuous-time stochastic nonlinear dynamics with an unmeasurable state, and there are additive and multiplicative noises along with information exchange among the agents. We propose admissible distributed observation strategies and cooperative output feedback control strategies based on the certainty equivalence principle. By using output regulation theory and stochastic analysis, we prove the existence of admissible distributed observation and cooperative control strategies to ensure mean square bounded output tracking, under certain conditions on the system dynamics and noise intensity.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Automation & Control Systems
Dacai Liu, Zhi Liu, C. L. Philip Chen, Yun Zhang
Summary: This paper proposes a novel adaptive fuzzy prescribed-time containment control method for nonlinear functions, which can achieve the tracking of system states and the containment of errors with prescribed performance within a prescribed time.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2021)
Article
Automation & Control Systems
Wei Liu, Jie Huang
Summary: This article studies the cooperative output regulation problem for heterogeneous linear multi-agent systems using the distributed sampled-data control approach. Two cases are considered: one with a constant state of the leader system, and the other with a time-varying state with bounded derivative. Solutions are provided for both cases, and two examples are used to illustrate the results.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2021)
Article
Automation & Control Systems
Dong Liang, Yi Dong, Chaoli Wang
Summary: This paper proposes a prescribed-time distributed control method to solve the cooperative output regulation problem for linear multi-agent systems with external disturbances. A distributed observer is designed to estimate the state of the dynamic leader in fixed time, independent of initial conditions. Then, a time-varying distributed control law is designed based on the parametric Lyapunov function technique, improving the transient performance of the closed-loop system.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Automation & Control Systems
Jie Zhang, Da -Wei Ding, Yingying Ren, Xinmiao Sun
Summary: This paper investigates the distributed robust group output synchronization problem of heterogeneous uncertain linear leader-follower multi-agent systems. A new group synchronization framework based on output regulation technique is proposed to achieve cooperative tracking with multiple targets. The paper introduces a distributed exosystem observer based on the algebraic Riccati inequality to obtain the information of exosystems. Additionally, the paper presents distributed control laws, including dynamic state feedback control protocol under an acyclic directed graph and dynamic output feedback control protocol under a general directed graph, to compensate for parameter uncertainties.
Article
Computer Science, Artificial Intelligence
Maryam Shahriari-Kahkeshi, Nader Meskin
Summary: This paper presents an adaptive cooperative control scheme for uncertain nonlinear multi-agent systems, using a Nussbaum function and an adaptive TSK-type fuzzy system to handle unknown factors and designing an adaptive distributed controller. Stability analysis shows that the consensus error can be made arbitrarily small by the proper selection of design parameters.
Article
Automation & Control Systems
Shimin Wang, Jie Huang
Summary: This paper investigates the cooperative output regulation problem of linear heterogeneous multi-agent systems influenced by an uncertain leader system, introducing an adaptive distributed observer for estimating the unknown parameter vector of the leader system. The problem is further addressed through distributed state feedback control law and distributed measurement output feedback control law utilizing the adaptive distributed observer.
INTERNATIONAL JOURNAL OF CONTROL
(2021)
Article
Computer Science, Artificial Intelligence
F. Baghbani, M-R Akbarzadeh-T, M-B Naghibi Sistani
Summary: This paper introduces an emotional neural network for multi-agent systems, with properties of fast response and learning. It approximates uncertain dynamics in an emotional way and describes interactions among agents using artificial potential functions.
Article
Automation & Control Systems
Yi Jiang, Weinan Gao, Jin Wu, Tianyou Chai, Frank L. Lewis
Summary: This paper proposes a novel control approach to solve the cooperative H infinity output regulation problem for linear continuous-time multi-agent systems (MASs). A distributed feedforward-feedback controller is developed to achieve asymptotic tracking and reject disturbances. A value iteration algorithm is proposed to learn the optimal feedback control gain and feedforward control gain using online data. The effectiveness of the proposed approach is demonstrated through numerical analysis.
Article
Automation & Control Systems
Penghai Wen, Min Wang, Shi-Lu Dai
Summary: This paper investigates distributed cooperative learning event-triggered control for discrete-time strict-feedback nonlinear multi-agent systems. It proposes a clever system decomposition method and constructs two interaction typologies to guarantee the weight convergence, reference trajectory tracking, and neural estimated weight convergence. The proposed scheme has advantages including small data storage space, low communication burden, and good generalization ability.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Automation & Control Systems
Hanfeng Li, Lihua Xie, Xianfu Zhang, Weihao Pan
Summary: This article focuses on the distributed consensus control problem for nonlinear multi-agent systems subject to sensor uncertainty. A new time-varying gain approach is proposed to achieve leader-follower consensus of nonlinear multi-agent systems and handle the unknown growth rate and uncertain sensor sensitivity.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Automation & Control Systems
Jie Zhang, Da-Wei Ding, Xinmiao Sun, Youyi Wang
Summary: This article investigates the cooperative fault-tolerant control problem of leader-follower multiagent systems with process faults and heterogeneous Lipschitz nonlinearity, utilizing output regulation theory. Distributed observers are designed to estimate the states and faults of all followers, with an adaptive distributed observer introduced to estimate the leader's state. A new distributed FTC protocol is then constructed based on the estimations, nonlinear regulator equation, and algebraic Riccati equation to ensure successful leader tracking despite process faults.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2021)
Article
Automation & Control Systems
Xiaoyu Luo, Chengcheng Zhao, Chongrong Fang, Jianping He
Summary: This paper investigates the problem of false data injection attacks in multi-agent dynamical systems and proposes FDI attack set selection algorithms to maximize the convergence error by finding the optimal subset of compromised agents.
Article
Automation & Control Systems
Nitin K. Singh, Abhisek K. Behera
Summary: In this paper, a twisting observer is proposed for robustly estimating the states of a second-order uncertain system. The observer approximates the unknown sign term for the non-measurable state with a delayed output-based switching function, and achieves the desired steady-state accuracy by controlling the delay parameter. The application of the observer to output feedback stabilization is also discussed.
Article
Automation & Control Systems
Alexander Aleksandrov
Summary: This paper investigates the absolute stability problem for positive Persidskii systems with delay, proposes a special construction method for diagonal Lyapunov-Krasovskii functionals, and derives a criterion for the existence of such functionals guaranteeing the absolute stability, as well as obtaining sufficient conditions for a family of time-delay Persidskii systems to construct a common diagonal Lyapunov-Krasovskii functional. The efficiency of the developed approaches is demonstrated through four examples.
Article
Automation & Control Systems
Noureddine Toumi, Roland Malhame, Jerome Le Ny
Summary: This paper addresses large multi-agent dynamic discrete choice problems using a linear quadratic mean field games framework. The model incorporates the features where agents have to reach a predefined set of possible destinations within a fixed time frame and running costs can become negative to simulate crowd avoidance. An upper bound on the time horizon is derived to prevent agents from escaping to infinity in finite time. The existence of a Nash equilibrium for infinite population and its epsilon-Nash property for a large but finite population are established. Simulations are conducted to explore the model behavior in various scenarios.
Article
Automation & Control Systems
Philippe Schuchert, Vaibhav Gupta, Alireza Karimi
Summary: This paper presents the design of fixed-structure controllers for the As2 and Asw synthesis problem using frequency response data. The minimization of the norm of the transfer function between the exogenous inputs and performance outputs is approximated through a convex optimization problem involving Linear Matrix Inequalities (LMIs). A general controller parametrization is used for continuous and discrete-time controllers with matrix transfer function or state-space representation. Numerical results show that the proposed data-driven method achieves performance equivalent to model-based approaches when a parametric model is available.
Correction
Automation & Control Systems
Zhijun Guo, Gang Chen
Article
Automation & Control Systems
Matteo Della Rossa, Thiago Alves Lima, Marc Jungers, Raphael M. Jungers
Summary: This paper presents new stabilizability conditions for switched linear systems with arbitrary and uncontrollable underlying switching signals. The study focuses on two specific settings: the robust case with completely unknown and unobservable active mode, and the mode-dependent case with controller depending on the current active switching mode. The technical developments are based on graph-theory tools and path-complete Lyapunov functions framework, enabling the design of robust and mode-dependent piecewise linear state-feedback controllers using directed and labeled graphs.
Article
Automation & Control Systems
Elena Petri, Romain Postoyan, Daniele Astolfi, Dragan Nesic, W. P. M. H. (Maurice) Heemels
Summary: This study investigates a scenario where a perturbed nonlinear system transmits its output measurements to a remote observer via a packet-based communication network. By designing both the observer and the local transmission policies, accurate state estimates can be obtained while only sporadically using the communication network.
Article
Automation & Control Systems
Jonas Krook, Robi Malik, Sahar Mohajerani, Martin Fabian
Summary: This paper proposes a method to synthesise controllers for cyber-physical systems subjected to disturbances, such that the controlled system satisfies specifications given as linear temporal logic formulas. The approach constructs a finite-state abstraction of the original system and synthesises a controller for the abstraction. It introduces the robust stutter bisimulation relation to account for disturbances and uncertainty, ensuring that related states have similar effects under the same controller. The paper demonstrates that the existence of a controller for the abstracted system implies the existence of a controller for the original system enforcing the linear temporal logic formula.
Article
Automation & Control Systems
Clement Chahbazian, Karim Dahia, Nicolas Merlinge, Benedicte Winter-Bonnet, Aurelien Blanc, Christian Musso
Summary: The paper derives a recursive formula of the Fisher information matrix on Lie groups and applies it to nonlinear Gaussian systems on Lie groups for testing. The proposed recursive CRLB is consistent with state-of-the-art filters and exhibits representative behavior in estimation errors. This paper provides a simple method to recursively compute the minimal variance of an estimator on matrix Lie groups, which is fundamental for implementing robust algorithms.
Article
Automation & Control Systems
Yiheng Fu, Pouria Ramazi
Summary: This study investigates the characteristics of decision fluctuations in heterogeneous populations and explores the uncertainties in imitation behavior. The findings are important for understanding the bounded rationality nature of imitation behaviors.
Article
Automation & Control Systems
Lars A. L. Janssen, Bart Besselink, Rob H. B. Fey, Nathan van de Wouw
Summary: This paper introduces a mathematical relationship between the accuracy of reduced-order linear-time invariant subsystem models and the stability and accuracy of the resulting reduced-order interconnected linear time-invariant model. This result can be used to directly translate the accuracy characteristics of the reduced-order subsystem models to the accuracy properties of the interconnected reduced-order model, or to translate accuracy requirements on the interconnected system model to accuracy requirements on subsystem models.
Article
Automation & Control Systems
Piyush Gupta, Vaibhav Srivastava
Summary: We study the optimal fidelity selection for a human operator servicing tasks in a queue, considering the trade-off between high-quality service and penalty due to increased queue length. By modeling the operator's cognitive dynamics and task fidelity, we determine the optimal policy and value function numerically, and analyze the structural properties of the optimal fidelity policy.
Article
Automation & Control Systems
Lukas Schwenkel, Alexander Hadorn, Matthias A. Mueller, Frank Allgoewer
Summary: In this work, the authors study economic model predictive control (MPC) in periodic operating conditions. They propose a method to achieve optimality by multiplying the stage cost by a linear discount factor, which is easy to implement and robust against online changes. Under certain assumptions, they prove that the resulting linearly discounted economic MPC achieves optimal asymptotic average performance and guarantees practical asymptotic stability of the optimal periodic orbit.
Article
Automation & Control Systems
Taher Ebrahim, Sankaranarayanan Subramanian, Sebastian Engell
Summary: We propose a robust nonlinear model predictive control algorithm for dynamic systems with mixed degrees of freedom. This algorithm optimizes both continuous and discrete manipulated variables, enhancing closed-loop performance. Our approach relies on a computationally efficient relaxation and integrality restoration strategy and provides sufficient conditions to establish recursive feasibility and guarantee robust closed-loop stability. The effectiveness of the approach is demonstrated through two nonlinear simulation examples.