Vehicle Trajectory Tracking Control Using MIMO-MPC Combined with IMM-AUKF Road Adhesion Coefficient Estimation

Abstract

This study investigates a simulation-based estimation–control chain that integrates an interactive multiple model adaptive unscented Kalman filter (IMM-AUKF) with a multiple-input multiple-output model predictive controller (MIMO-MPC). A seven-degree-of-freedom vehicle model and a Pacejka tire model are used to represent nonlinear vehicle dynamics. The controller updates model and regression region steering constraints from the estimated adhesion state and jointly allocates front/rear steering and longitudinal force commands. The revised experiments use a 0.5 ms plant integration step and a consistent 20 ms estimator/controller update. Under high-adhesion double lane change (DLC), the proposed chain lowers speed RMSE from 0.7950 to 0.1833 m/s and mean adhesion estimation RMSE from 0.1567 to 0.0812. Under variable-adhesion single lane change (SLC), lateral RMSE decreases from 0.1816 to 0.1649 m, speed RMSE from 0.7983 to 0.2505 m/s, and mean adhesion estimation RMSE from 0.1622 to 0.0949. Heading error is not uniformly improved and is reported as a design trade-off. These results provide reproducible simulation evidence, while hardware-in-the-loop and real-vehicle validation remain future work.