The Integrated Trajectory Tracking, Yaw Stability and Roll Stability Model Predictive Control for Autonomous Vehicle in Limited Handling Condition

Abstract

<div class="section abstract"><div class="htmlview paragraph">In the current literature, the research studies on the trajectory tracking control and stability control strategy for autonomous vehicles in limited condition mostly focus on the yaw plane control, but few of the studies have considered the combined control performance of trajectory tracking, yaw and roll stability, and the roll stability is critical under the extreme cornering condition for autonomous vehicles. Aiming at the above shortages, this study designs the model predictive control (MPC) strategy for the autonomous vehicles under the limited handling condition, which integrates the front and rear wheel active steering control, four-wheel independent drive and braking control and active suspension control to comprehensively improve the trajectory tracking accuracy, yaw plane stability and roll plane stability of the vehicle under the extreme condition. In the internal prediction model of the MPC, the yaw plane dynamics, roll plane dynamics and suspension system models are considered to better coordinate the yaw plane and roll plane dynamics control. Also the different control delays of steering, driving, braking and suspension control actuators are considered in the model. In addition, in order to improve the vehicle yaw stability, the soft constraints of wheel longitudinal slip and lateral side-slip angle are designed in the optimization objective function of the MPC. Furthermore, based on the analysis on the coupling effect of the steering, traction or brake and active suspension control on the trajectory tracking and vehicle dynamics stability, the scaling factors of MPC optimization cost function are normalize and carefully tuned to achieve the best performance. Finally, the effectiveness and computational efficiency of the designed integrated MPC strategy is verified by simulation based on high fidelity vehicle dynamics model.</div></div>