Model Predictive Controller for Path Tracking and Obstacle Avoidance Manoeuvre on Autonomous Vehicle

Some challenging control design problems include non-linear vehicle dynamics, fast sampling time and limited computing resources on automated hardware. MPC has the ability to systematically consider nonlinearity, future predictions and operating constraints of the control system framework. One problem for autonomous vehicles operating on toll roads must be able to do a satisfactory tracking path when avoiding obstacles so that accidents do not occur. This paper will discuss designing tracking path controllers using a predictive controller (MPC) model based on scenario avoidance obstacle on the highway with several variations in speed. The trajectory has been predetermined and the controller must be able to autonomously avoid static obstacles on the road and can track the desired trajectory by controlling the front steering angle of the vehicle. This approach discusses solving a single nonlinear MPC problem for following trajectories and avoiding static obstacle. The vehicle model was developed based on 3 DOF non-linear vehicle model. This controller model was developed based on X, Y global position and yaw rate to get input in the form of steering to the vehicle dynamic system. For path tracking strategy, comparisons with the Stanley controller are done to analyse MPC reliability as nonlinear controller in low and middle speed scenario. Simulation results show that the MPC controller has the advantage of a tracking path that is good at mid and high speeds.