Event-triggered optimal containment control for multi-agent systems subject to state constraints via reinforcement learning

Xu, Jiahong; Wang, Lijie; Liu, Yang; Xue, Hong

doi:10.1007/s11071-022-07513-4

Cited by 13 publications

(3 citation statements)

References 42 publications

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“…The utility function is considered a weight that reflects the optimal balance among the control effort, tracking error, and potential disturbances. In [11], the control input and the external disturbance are seen as opposites with different goals, and the optimal controller can be obtained by solving the Hamilton-Jacobi-Isaacs (HJI) equation. Unlike the continuous-time system, the discrete-time Hamilton-Jacobi-Bellman (HJB) equation is 1.…”

Section: Introductionmentioning

confidence: 99%

Event-triggered Optimal Consensus for Discrete-time Nonlinear Multiagent Systems with DoS attacks via Reinforcement Learning Method

Liao,

Wang,

Wang

et al. 2024

Preprint

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This paper focuses on the event-triggered optimal control problem of discrete-time nonlinear multiagent systems (MASs) via the reinforcement learning method. Distinguished from the existing consensus protocols for discrete-time multiagent systems under the ideal communication scenario, the considered model suffers from input saturation, unknown external disturbance, and denial-of-service (DoS) attacks. According to the approximation capability of the radial basis function neural network(RBF NN), the disturbance network is established to tackle the effect of the unknown disturbance on consensus issues. Subsequently, the composite controller is derived with the reinforcement learning strategy to deal with the DoS attacks, where the update frequency of the actor-critic networks and controllers is determined by the novel event-triggered mechanism. Finally, two simulations are formulated to verify the feasibility of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Event-triggered Optimal Consensus for Discrete-time Nonlinear Multiagent Systems with DoS attacks via Reinforcement Learning Method

Liao,

Wang,

Wang

et al. 2024

Preprint

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“…Therefore, an improved partially unknown system control method was presented in terms of integral reinforcement learning (IRL) in Su et al [18]. So far, there are several similar names for the method of ADP, such as adaptive critic design [17], neuron-dynamic programming [19], reinforcement learning [20][21][22][23], and approximate dynamic programming [24].…”

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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“…In [6], the experience replay was used in combination with ADP, using the past and current data concurrently. In [7]- [9], the adaptive optimal controller was designed by the online actor-critic learning, in order to solve the robust optimal control problem for a class of nonlinear systems. In [10], a model-free λ-policy iteration (λ-PI) was presented for the discrete-time linear quadratic regulation (LQR) problem.…”

mentioning

confidence: 99%

Policy Gradient Adaptive Dynamic Programming for Model-Free Multi-Objective Optimal Control

Zhang,

Li,

Wang

et al. 2024

IEEE/CAA J. Autom. Sinica

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Event-triggered optimal containment control for multi-agent systems subject to state constraints via reinforcement learning

Cited by 13 publications

References 42 publications

Event-triggered Optimal Consensus for Discrete-time Nonlinear Multiagent Systems with DoS attacks via Reinforcement Learning Method

Event-triggered Optimal Consensus for Discrete-time Nonlinear Multiagent Systems with DoS attacks via Reinforcement Learning Method

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

Policy Gradient Adaptive Dynamic Programming for Model-Free Multi-Objective Optimal Control

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