Optimal Control for Stochastic Systems With Multiple Controllers of Different Information Structures

Qi, Qingyuan; Xie, Lihua; Zhang, Huanshui

doi:10.1109/tac.2020.3035625

Cited by 28 publications

(20 citation statements)

References 24 publications

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“…Reference 10 studied the optimal economic stabilization policies under decentralized control and conflicting objectives. References 11‐13 considered the decentralized control for networked control systems with asymmetric information. Noticing that decentralized optimal controllers are generally in the feedback form of estimation.…”

Section: Introductionmentioning

confidence: 99%

Team optimal decentralized estimation by multiple agents with asymmetric information

Sun

Wang

et al. 2023

Optim Control Appl Methods

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In this paper, we are concerned with the team optimal decentralized state estimation problem where the state is transmitted to a remote agent with packet dropouts and is observed by local agents with additive noises. In particular, the information of the remote agent is available to all local agents with hierarchical relationships, which leads to an asymmetric information structure. The main contribution is to derive the analytical expressions for the team optimal decentralized state estimation. Different from the existing decentralized estimation results, the novelty of the paper is to comprehensively investigate the influence of packet dropouts and additive noises, which occur frequently in networked control systems.

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Section: Introductionmentioning

confidence: 99%

Team optimal decentralized estimation by multiple agents with asymmetric information

Sun

Wang

et al. 2023

Optim Control Appl Methods

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“…For the last couple of decades, a great deal of research has been done on addressing the optimal control problem for linear and nonlinear systems [4][5][6][7][8][9][10][11][12][13]. The sufficient condition for the existence of optimality is given by seeking the solution of the Hamilton-Jacobi-Bellman (HJB) equation.…”

Section: Introductionmentioning

confidence: 99%

“…In order to process the intractable nonlinear models of physical systems, Li et al [9] studied an efficient optimal control scheme to settle the status unmeasured problem of nonlinear systems. Besides, optimal control is extended to a series of more complex nonlinear systems including switched systems [5], stochastic systems [10], and strict-feedback systems [6,8].…”

Section: Introductionmentioning

confidence: 99%

Finite‐time optimal tracking control for a class of nonlinear systems with prescribed performance

Wang

Liu

et al. 2023

Asian Journal of Control

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In this paper, a finite‐time optimal tracking control scheme based on integral reinforcement learning is developed for partially unknown nonlinear systems. In order to realize the prescribed performance, the original system is transformed into an equivalent unconstrained system so as to a composite system is constructed. Subsequently, a modified nonlinear quadratic performance function containing the auxiliary tracking error is designed. Furthermore, the technique of experience replay is used to update the critic neural network, which eliminates the persistent of excitation condition in traditional optimal methods. By combining the prescribed performance control with the finite‐time optimization control technique, the tracking error is driven to a desired performance in finite time. Consequently, it has been shown that all signals in the partially unknown nonlinear system are semiglobally practical finite‐time stable by stability analysis. Finally, the provided comparative simulation results verify the effectiveness of the developed control scheme.

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“…Each individual updates their states through neighbor information, resulting in collective behaviors such as clustering and separation. Especially, many works of multi-agent systems had emerged, such as consensus Wang, Liu, Ji and Han (2020); Liu, Ji and Zhang (2020a); Sun, Ji and Liu (2020); Sun, Ji, Qi and Ma (2019), controllability Lu, Ji and Zhang (2020); Qu, Ji and Shi (2020); Ji, Lin, Cao, Qi and Ma (2021); Guan, Tian and Wang (2019); ; Liu, Ji and Ma (2020b); Lou, Ji and Qu (2020), observability ; ; Liu, Xu, Su, Wu and Bai (2019), stabilisability Guan and Kong (2019), optimal control Sun, Liu, Li and Niu (2018); Sun, Liu and Li (2020); Sun, Liu and Li (2021); Han, Ji and Qi (2020); Qi, Xie and Zhang (2020); Qi, Zhang and Wu (2018) and model-free adaptive control Hou and Xiong (2019).…”

Section: Introductionmentioning

confidence: 99%

Controllability and observability of linear multi-agent systems over matrix-weighted signed networks

Zhao¹,

Ji²,

Liu³

et al. 2022

Preprint

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In this paper, the controllability and observability of linear multi-agent systems over matrixweighted signed networks are analyzed. Firstly, the definition of equitable partition of matrixweighted signed multi-agent system is given, and the upper bound of controllable subspace and a necessary condition of controllability are obtained by combining the restriction conditions of the coefficient matrix and matrix weight for the case of fixed and switching topologies, respectively. The influence of different selection methods of coefficient matrices on the results is discussed. Secondly, for the case of heterogeneous systems, the upper bound of controllable subspace and the necessary condition of controllability are obtained when the dynamics of individuals in the same cell are the same. Thirdly, sufficient conditions for controllable and uncontrollable union graphs are obtained by taking advantage of the concept of switched systems and equitable partitions, respectively. Finally, a necessary condition of observability is obtained in terms of the dual system and the constraints of the coefficient matrix, and the relationship between the observability and the controllability of the matrix-weighted signed multi-agent systems is discussed.

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Optimal Control for Stochastic Systems With Multiple Controllers of Different Information Structures

Cited by 28 publications

References 24 publications

Team optimal decentralized estimation by multiple agents with asymmetric information

Team optimal decentralized estimation by multiple agents with asymmetric information

Finite‐time optimal tracking control for a class of nonlinear systems with prescribed performance

Controllability and observability of linear multi-agent systems over matrix-weighted signed networks

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