Event-Triggered Reinforcement Learning Control for Switching Fuzzy Vehicle Lateral Dynamics

This paper investigates the exponential$H_{\infty } $stability of switching fuzzy vehicle lateral dynamics using an event-triggered reinforcement learning control strategy, which is fundamental for improving the performance, safety, and efficiency of transportation systems. A newly constructed vehicle model is proposed to comprehensively reflect the vehicle’s operating environment. By combining an event-triggered mechanism with our previously published switching law, frequent switchings are achieved without the restrictive condition$\tau _{a} \gt T$. Reinforcement learning further reduces event triggers, lowering communication costs while maintaining system performance. Moreover, the maximum–minimum dwell time method is used to derive a non-weighted$L_{2}$norm constraint, preserving the original interpretation of the$L_{2}$norm inequality. Finally, a numerical example demonstrates the effectiveness of the proposed approach.