Event-Triggered Adaptive Fuzzy Approach-Based Lateral Motion Control for Autonomous Vehicles

As the level of vehicle intelligence increases, the amount of communication data is also constantly increasing. At the same time, there is parameter uncertainty and unknown disturbances in the vehicle system modeling poses challenges to the precise control of vehicle states. To address these challenges, an event-triggered adaptive fuzzy control is presented. The fuzzy logic system (FLS) approximates the boundary of uncertain parameters and nonlinearity in the control system, which ensures control accuracy and robustness of the system. Additionally, to reduce the communication burden of the vehicle, an event-triggering strategy with relative threshold values is designed. This controller ensures that the control error converges to a neighborhood near the zero point while avoiding the Zeno behavior. The experimental results indicate that the control strategy ensures the control accuracy and reduces the communication burden. This approach provides an effective solution for designing lateral motion controllers for autonomous vehicles.