含输入饱和的自动驾驶汽车预设性能自适应控制

This paper aims to improve the transient and steady-state performances of autonomous vehicle systems with input saturation and unknown perturbations. Firstly, a coordinated controller based on the sliding mode control and the prescribed performance control is designed considering the coupling between the lateral and longitudinal motion dynamics. To address the possible input saturation, an auxiliary system is designed to adjust the prescribed performance boundaries when saturation occurs, so that the tracking errors always adhere to the performance constraint. Consequently, it avoids the possible instability when the errors cross the performance boundaries. Finally, the neural network is introduced to approximate and compensate for the model uncertainty and external interference, and an online identification scheme based on a composite learning algorithm is proposed to train the neural network. The stability of the closed-loop system is strictly proved by Lyapunov approach, and the effectiveness of the proposed identification and control scheme is verified by simulation. The coordinated controller can be used to ensure the prescribed trajectory tracking performance in the presence of strong coupling characteristics, model uncertainty, and external interference.