Event-Driven H∞-Constrained Control Using Adaptive Critic Learning
Event-Driven H∞-Constrained Control Using Adaptive Critic Learning
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DOI：
10.1109/tcyb.2020.2972748
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发表时间：
2020-02
影响因子：
11.8
通讯作者：
Xiong Yang;Haibo He
中科院分区：
文献类型：
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作者：
Xiong Yang;Haibo He
本文考虑了事件驱动的$ h _ {\ infty} $连续时间非线性系统具有非对称输入约束的问题。与折扣的非二次成本功能相关，我们介绍了与事件驱动的汉密尔顿– jacobi – isaacs方程（HJIE）相关的，这是两人的零和零游戏。行为排除在当前的事件触发条件与现有文献不同，因为它可以使阈值非负问题，而无需正确选择规定的灾难衰减水平。使用单个关键网络来解决事件驱动的HJIE，并基于Lyapunov方法使用历史和瞬时状态数据来调整其权重参数。保证。最后，提出了非线性工厂的模拟，以验证开发的事件驱动的$ h _ {\ infty} $控制策略。
This article considers an event-driven $H_{\infty }$ control problem of continuous-time nonlinear systems with asymmetric input constraints. Initially, the $H_{\infty }$ -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 $H_{\infty }$ control strategy.