基于模型预测路径积分和输出调节的层级运动控制

Due to the limitation of the computing power of the vehicle platform,there is an irreconcilable contradiction between the long-term control/prediction time domain and the short-term control step. In this paper,a necessary condition is derived to decouple the translational motion from yawing motion based on the time-scale separation. Consequently,the translational motion is regulated over an extended control horizon to generate a human-like tracking trajectory. The yawing motion is regulated based on a more accurate dynamic model and a shorter control cycle. In addition,model predictive path integral(MPPI)strategy is used to mitigate the computational burden of nonlinear motion planning through sampling-based optimization. Finally,a model predictive output regulator is proposed to solve the underactuated control problem in vehicle lateral dynamics and reduce steady-state errors in yaw angel. Theoretical analysis and simulation results show that the proposed method enhances computing efficiency,improves the parameters adaptability and steering smoothness and reduces the lateral jerk by an average of 50% in all driving scenarios.