Trajectory Tracking Control of Distributed Driving Intelligent Vehicles Based on Adaptive Variable Parameter MPC

doi:10.3901/JME.2024.06.363

Abstract

Abstract: To coordinate the trajectory tracking accuracy and stability of distributed drive intelligent driving vehicles and improve the adaptive capability of the control algorithm to uncertainties such as speed perturbations or road surface adhesion coefficient changes, the square rooting cubature Kalman filter (SRCKF) based tire lateral force estimation is used to online correct the tire cornering stiffness. The MPC control strategy based on T-S fuzzy variable weight is proposed to realize the trajectory tracking control. The front wheel steering angle and longitudinal drive force of each wheel are used as control variables under the characteristics of distributed drive intelligent vehicle with all-wheel independent controllability. The real-time lateral error and yaw error are used as fuzzy inputs to optimize the MPC objective function weights online by T-S fuzzy control and coordinate the influence of the weight matrix on the trajectory tracking accuracy and stability. The effectiveness of the proposed control strategy under various operating conditions is verified by simulation and experimental data. It is shown that compared with the traditional MPC control, the proposed adaptive variable parameter MPC (AMPC) has good tracking effect on 80-120 km/h double lane change, wet road and docking road conditions. AMPC can effectively improve the trajectory tracking accuracy, and can coordinate the control tracking accuracy and stability, meanwhile reduce the fluctuation of control output volume.

Key words: Intelligent driving, trajectory tracking, tire force estimation, distributed driving, condition adaption

CLC Number:

U461

YANG Zekun, LI Shaohua, WANG Zhenfeng. Trajectory Tracking Control of Distributed Driving Intelligent Vehicles Based on Adaptive Variable Parameter MPC[J]. Journal of Mechanical Engineering, 2024, 60(6): 363-377.

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