Optimized Active Disturbance Rejection Control for Switched Systems: A Triggered-Learning Approach

This article studies active disturbance rejection control (ADRC) for uncertain switched systems under triggered learning. The novel triggered-learning ADRC framework optimizes the ADRC performance of switched systems through reinforcement learning (RL) and enables “on-demand” updates of neural networks with guidance from a predesigned trigger. The innovation of this article is mainly reflected in four aspects: First, the RL-based gain automatic update mechanism (i.e., the dual-gain optimization mechanism) optimizes the performance of extended state observer and controller; the optimal policy is obtained from the experience-based deep deterministic policy gradient with self-learning ability. Second, the adaptive performance-triggered strategy guides the update of dual-gain; the on-demand triggering judgment is achieved by comparing cost functions that reflect the tracking control performance. Third, the proposed theoretical analysis method proves that the learning mechanism can enhance the closed-loop system performance. Fourth, the constructed ADRC-based switching law accelerates the convergence of system tracking error. Finally, a comparative simulation example demonstrates the effectiveness of the proposed method.