Reinforcement Learning-Based Switching Event-Triggered Control for MWMR Under Asymmetric Actuator Saturation

This paper investigates the optimal tracking control problem for a Mecanum-wheeled mobile robot subject to asymmetric actuator saturation. Within the command-filtered backstepping framework, a switching event-triggered control strategy based on reinforcement learning is proposed. First, a compensation system incorporating an anti-saturation term is designed to mitigate both filtering errors and actuator saturation. Then, a novel switching event-triggered mechanism is developed based on the actuator saturation status. It dynamically updates the control signal during unsaturated phases while suspending updates during saturation phases, thereby significantly reducing communication frequency. To achieve optimal control, a reinforcement learning algorithm is constructed by leveraging the function approximation capabilities of neural networks, without relying on prior knowledge of system dynamics. Based on Lyapunov stability theory, it is strictly proven that all signals of the closed-loop system are uniformly ultimately bounded. The simulation results demonstrate that the proposed method effectively reduces data transmission compared with conventional dynamic event-triggered mechanisms, while maintaining satisfactory tracking performance.