A hybrid control strategy for path tracking of intelligent vehicles is developed to address the limitations of traditional control algorithms in coordinating the control performance of intelligent vehicles under varying working conditions. The strategy combines error-free kinematic model predictive control (MPC) and no sinusoidal terminal sliding mode control (NTSMC) to account for the distinct system characteristics of intelligent vehicles during high-speed and low-speed steering operations. The control strategy employs error-free kinematic model predictive control for low speed and large curvature conditions and non-singular terminal sliding model control for high speed and small curvature conditions. The path-tracking control mode is determined by the vehicle speed, and the switching coordination controller is designed to achieve smooth switching of the lateral control system. And the yaw stability controller (YSC) is designed to ensure yaw stability during high-speed turns. Finally, based on the hardware-in-the-loop (HIL) platform, the hybrid control strategy for intelligent vehicle path tracking was simulated and verified. The simulation and test results show that the designed hybrid control strategy can ensure the path tracking performance of the intelligent vehicle at any speed with good tracking accuracy, real-time performance, and vehicle driving stability.
Zhang et al. (Thu,) studied this question.