4.7 Article

Event-Triggered Robust Stabilization of Nonlinear Input-Constrained Systems Using Single Network Adaptive Critic Designs

期刊

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMC.2018.2853089

关键词

Robustness; Optimal control; Nonlinear systems; Robust control; Perturbation methods; Adaptive systems; Adaptive critic designs (ACDs); adaptive dynamic programming (ADP); event-triggered control (ETC); input constraints; neural network (NN); reinforcement learning (RL)

资金

  1. National Natural Science Foundation of China [61503379]
  2. China Scholarship Council under the State Scholarship Fund
  3. National Science Foundation [CMMI 1526835, ECCS 1731672]

向作者/读者索取更多资源

In this paper, we study the event-triggered robust stabilization problem of nonlinear systems subject to mismatched perturbations and input constraints. First, with the introduction of an infinite-horizon cost function for the auxiliary system, we transform the robust stabilization problem into a constrained optimal control problem. Then, we prove that the solution of the event-triggered Hamilton-Jacobi-Bellman (ETHJB) equation, which arises in the constrained optimal control problem, guarantees original system states to be uniformly ultimately bounded (UUB). To solve the ETHJB equation, we present a single network adaptive critic design (SN-ACD). The critic network used in the SN-ACD is tuned through the gradient descent method. By using Lyapunov method, we demonstrate that all the signals in the closed-loop auxiliary system are UUB. Finally, we provide two examples, including the pendulum system, to validate the proposed event-triggered control strategy.

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