基于线性时变模型预测控制的实时抗噪高速车辆运动控制

A two-layer control architecture is proposed for the real-time high-precision motion control of high-speed autonomous vehicles, in which an upper layer consists of a path point filter based on a path point Cost function and a velocity planner based on tire force analysis in both lateral and longitudinal directions, and a lower layer consists of a path tracking controller predicted by a linear time-varying dynamic model and a velocity controller. A least mean square (LMS) adaptive state estimator is introduced to enhance the system's noise immunity. The path point filter improves the operation speed and reduces the loss of crucial information during selection, and the velocity planner generates the optimal speed curve under the premise of safe driving. The path tracking controller considers the tracking deviation soft constraint to improve the tracking effect. The LMS state estimator estimates the lateral velocity and yaw rate online based on an online-corrected dynamic model. A dSPACE-TX2 hardware-in-the-loop simulation environment is constructed, and the proposed path tracking architecture is compared with traditional motion tracking control under high-speed and double lane change scenarios. The hardware-in-the-loop simulation results demonstrate that the proposed motion controlling architecture improves the noise immunity and the tracking accuracy of 21% while meeting the 50 Hz high-frequency control requirement.