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Event-triggered optimal consensus control for stochastic multi-agent systems based on adaptive dynamic programming
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DOI：10.1016/j.jfranklin.2025.108226.png)
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Abstract
En 中文
This paper studies the event-triggered optimal consensus control problem of nonlinear stochastic multi-agent systems (MASs) and designs an optimal control strategy using the adaptive dynamic programming method. To effectively reduce the communication load and computational complexity, a new event-triggering scheme is designed. The neural network (NN) is utilized to approximate the optimal value function, and the approximate Hamilton-Jacobi-Bellman equation is obtained. Subsequently, an approximate solution of the equation is adopted to design the optimal control strategy for the MASs. Moreover, the Lyapunov stability principle is applied to analyze the stability of the nonlinear stochastic MASs. It is proved that under the proposed event-triggered mechanism, the designed optimal control strategy, and the NN weight update rule, both the consensus error and the NN weight estimation error are ultimately uniformly bounded, and their respective bounds are provided respectively. Additionally, the Zeno behavior is excluded. Finally, the validity and practicability of the proposed control strategy are verified through two simulation examples.
Journal
J
IF:
4.2
Papers:
822
Citations:
0