Curved path tracking control is one of the most important functions of autonomous vehicles. First, small turning radius circular bends considering bend quadrant and travel direction restrictions are planned by polar coordinate equations. Second, an estimator of a vehicle state parameter and road adhesion coefficient based on an extended Kalman filter is designed. To improve the convenience and accuracy of the estimator, the combined slip theory, trigonometric function group fitting, and cubic spline interpolation are used to estimate the longitudinal and lateral forces of the tire model (215/55 R17). Third, to minimize the lateral displacement and yaw angle tracking errors of a four-wheel steering (4WS) vehicle, the front-wheel steering angle of the 4WS vehicle is corrected by a model predictive control (MPC) feed-back controller. Finally, CarSim® simulation results show that the 4WS autonomous vehicle based on the MPC feed-back controller can not only significantly improve the curved path tracking performance but also effectively reduce the probability of drifting or rushing out of the runway at high speeds and on low-adhesion roads.
MPC-based path tracking with PID speed control for autonomous vehicles
