Adaptive Critic Learning and Experience Replay for Decentralized Event-Triggered Control of Nonlinear Interconnected Systems
Adaptive Critic Learning and Experience Replay for Decentralized Event-Triggered Control of Nonlinear Interconnected Systems
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DOI：
10.1109/tsmc.2019.2898370
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发表时间：
2020-11
期刊：
影响因子：
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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
在本文中，我们为一类具有不确定互连的非线性系统开发了一种分散事件触发控制（ETC）策略。首先，我们证明整个系统的去中心化 ETC 策略可以用一组辅助子系统的最优 ETC 法则来表示。然后，在自适应批评学习的框架下，我们构建批评网络来求解与这些最优 ETC 定律相关的事件触发的 Hamilton-Jacobi-Bellman 方程。批评者网络中使用的权重向量通过使用梯度下降方法和经验重放（ER）技术来更新。借助ER技术，我们可以克服激励条件持续存在的困难。同时，通过使用经典的李亚普诺夫方法，我们证明了批评者网络中使用的估计权重向量最终是一致有界的。此外，我们证明了所获得的去中心化 ETC 可以迫使整个系统渐近稳定。最后，我们提出了一个互连的非线性设备来验证所提出的去中心化 ETC 方案。
In this paper, we develop a decentralized event-triggered control (ETC) strategy for a class of nonlinear systems with uncertain interconnections. To begin with, we show that the decentralized ETC policy for the whole system can be represented by a group of optimal ETC laws of auxiliary subsystems. Then, under the framework of adaptive critic learning, we construct the critic networks to solve the event-triggered Hamilton–Jacobi–Bellman equations related to these optimal ETC laws. The weight vectors used in the critic networks are updated by using the gradient descent approach and the experience replay (ER) technique together. With the aid of the ER technique, we can conquer the difficulty arising in the persistence of excitation condition. Meanwhile, by using classic Lyapunov approaches, we prove that the estimated weight vectors used in the critic networks are uniformly ultimately bounded. Moreover, we demonstrate that the obtained decentralized ETC can force the overall system to be asymptotically stable. Finally, we present an interconnected nonlinear plant to validate the proposed decentralized ETC scheme.