Path Tracking Control Based on the Prediction of Tire State Stiffness Using the Optimized Steering Sequence

This study proposes a linear time-varying model predictive control method based on tire state stiffness prediction for the path tracking using a steering decision sequence in a prediction horizon. A nonlinear UniTire model is employed to represent the nonlinear features of vehicle dynamics in critical situations. And the changing trend of tire state stiffness over the prediction horizon is constructed based on the steering decision sequence, which is the optimized solution of the previous execution step by the controller. Moreover, a method of adjusting the tire state stiffness is proposed to address the jittering in the process of linearization. Meanwhile, a nonlinear model predictive controller and a traditional linear time-varying model predictive controller are designed to verify the effectiveness of the proposed linearization method. Experimental results clearly show that this linearization method can considerably improve vehicle stability under extreme conditions.

Abstract licence: CC BY-NC-ND

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