Nonperiodic and Periodic Event-Triggered Online H∞ Control for Constrained Nonlinear Systems
针对带扰动的离散时间约束非线性系统，提出两种基于在线评论学习的非周期和周期事件触发控制方案，通过神经网络实现H∞最优控制，并用物理实例验证有效性。
This article presents two new event-triggered control (ETC) schemes based on the online critic learning technique, which aims at tackling the optimal regulation problem of discrete-time constrained nonlinear systems with the disturbance input. First, a novel stability criterion condition is designed to obtain an initial admissible policy pair by using an offline iterative method under the time-triggered control framework. Then, starting from the stability of the constrained system, a nonperiodic ETC method and a periodic ETC method are developed by adopting an online learning algorithm. In addition, four kinds of neural networks are constructed for the implementation of the event-based online <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> optimal control strategy. Finally, two experimental examples with physical backgrounds are provided to illustrate the effectiveness and superiority of the developed schemes.