RL-Based Adaptive Fuzzy Optimized Tracking Control for Constrained Switched Stochastic Nonlinear Systems: A Modified...

This study presents a reinforcement learning (RL)-based adaptive fuzzy event-triggered optimized tracking control strategy for slowly switched nonlinear systems with stochastic disturbances in the prescribed set-time performance. The designed optimized event-triggered mechanism for the subsystems effectively solves the asynchronous switching problem with no limit on the maximum asynchronous time. Moreover, the tracking performance of system can be optimized significantly using an RL strategy. Using the lemma proposed in the study (Lemma 3) and the normalized function, it is shown that under the performance constraint approach, the selection of the performance function is consistent with the control protocols. By adopting the modified admissible edge-dependent average dwell time method and the optimal controller, the boundedness of closed-loop system signals is proved, and the Zeno phenomenon does not occur. Finally, the superiority of the optimized strategy is verified using numerical simulations and a practical single-link manipulator.