Event-Based Constrained Robust Control of Affine Systems Incorporating an Adaptive Critic Mechanism
针对连续时间仿射非线性系统，提出一种结合事件触发、约束最优控制和神经网络学习的鲁棒控制策略，通过设计事件触发约束最优控制器实现输入受限下的非线性鲁棒状态反馈，并用评判神经网络进行学习逼近。
This paper focuses on establishing an event-based constrained robust control strategy for a class of continuous-time affine nonlinear systems by incorporating the adaptive critic mechanism (ACM). The main objective is to integrate the event-based framework, the constrained optimal control method, and the neural network learning ability, thereby achieving the nonlinear robust state feedback of input-constrained nonlinear systems under event-based environment. Through theoretical analysis, it is shown that the nonlinear robust control law subject to input limitations can be obtained by designing an event-based constrained optimal controller with respect to the nominal system. Then, the ACM is adopted to facilitate the constrained optimal control implementation, where a critic neural network is constructed to serve as the learning approximator. The system stability issue is proved by employing the Lyapunov theory and the constrained robust control performance is illustrated through simulation experiments of several dynamical plants.