Adaptive Model Predictive Control-Based Path Following Control for Four-Wheel Independent Drive Automated Vehicles

Abstract

Due to inevitable parameter uncertainties and disturbances, four-wheel independent drive automated vehicles (4WIDAVs) will produce tracking deviation during the path following process, which have a negative impact on driving safety. Meanwhile, the over-actuated feature of 4WIDAVs will also increase the deviation if not properly handled. To solve this problem, a specific adaptive model predictive control strategy for path following of 4WIDAVs is proposed. Firstly, to obtain a real-time and accurate vehicle dynamics model, the recursive least square method is used to estimate the time-varying uncertainty of tire cornering stiffness. Secondly, based on the real-time updating system model, the modified tube-based model predictive control method is applied to realize path following under the influence of the disturbance. Meanwhile, the compensating yaw moment for controlling vehicle is generated by the designed torque distribution algorithm, which makes full use of the over-actuated feature of 4WIDAVs. Finally, different maneuvers are performed both in simulation and experiment. Results show that the proposed strategy can achieve more accurate path following than the traditional model predictive control and linear quadratic regulator. Compared with the existing controller, the path following accuracy is improved by 41.6% and 60% in simulation and experiment, respectively. Therefore, the proposed strategy is proved to be effective, which provides a theoretical reference for vehicle control in reality.