A Decentralized Learning Control Scheme for Constrained Nonlinear Interconnected Systems Based on Dynamic Event-Triggered Mechanism
针对部分未知、带不对称输入约束和失配互联的非线性系统，提出一种基于动态事件触发条件的分散式学习控制方法，利用积分强化学习避免系统漂移动力学，并通过神经网络逼近值函数，证明闭环信号一致最终有界。
This article presents a decentralized learning control method for a class of partially unknown nonlinear systems with asymmetric control input constraints and mismatched interconnections via a novel dynamic event-triggering condition. By employing an integral reinforcement learning strategy, the system drift dynamics can be avoided in the learning process. Meanwhile, a critic neural network is designed to obtain the approximated value function and tuned by using the gradient descent approach. Furthermore, a novel dynamic event-triggering condition is designed to determine the occurrence of an event by introducing a dynamic variable. By using the Lyapunov theory, all signals in the closed-loop system are proved to be uniformly ultimately bounded. Finally, we present a nonlinear interconnected system and an interconnected power system to verify the effectiveness of the proposed method.