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Event-Driven H∞-Constrained Control Using Adaptive Critic Learning
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DOI：10.1109/TCYB.2020.2972748.png)
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摘要
En 中文
This article considers an event-driven H-infinity control problem of continuous-time nonlinear systems with asymmetric input constraints. Initially, the H-infinity-constrained control problem is converted into a two-person zero-sum game with the discounted nonquadratic cost function. Then, we present the event-driven Hamilton-Jacobi-Isaacs equation (HJIE) associated with the two-person zero-sum game. Meanwhile, we develop a novel event-triggering condition making Zeno behavior excluded. The present event-triggering condition differs from the existing literature in that it can make the triggering threshold non-negative without the requirement of properly selecting the prescribed level of disturbance attenuation. After that, under the framework of adaptive critic learning, we use a single critic network to solve the event-driven HJIE and tune its weight parameters by using historical and instantaneous state data simultaneously. Based on the Lyapunov approach, we demonstrate that the uniform ultimate boundedness of all the signals in the closed-loop system is guaranteed. Finally, simulations of a nonlinear plant are presented to validate the developed event-driven H-infinity control strategy.
Keyword:
Adaptive critic learning (ACL)
adaptive dynamic programming (ADP)
asymmetric constraints
event-driven H-infinity control
reinforcement learning (RL)
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期刊
IF:
10.5
论文数:
1.1W
被引数:
5.0W
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