Support Vector Machine Based Model Predictive Control for Vehicle Path Tracking Control

In this paper, a support vector machine (SVM) based model predictive control (MPC) strategy is proposed to enable the vehicle to track the reference path. Considering that learning nonlinear process using SVM is simple and the results are accurate and without over-fitting,the predictive model is trained by SVM. In order to retain the dynamic information on the basis of reducing the computational burden, the instantaneous linearization is applied to SVM model by Taylor expansion. Moreover, its accuracy is verified. Then the model predictive controller is obtained to achieve the vehicle path tracking control. Finally, the simulation results demonstrates the control performance of the proposed method.