Path Tracking of Autonomous Vehicles with Model Predictive Control (MPC) Optimized with Particle Swarm Optimization (PSO)

<div class="section abstract"><div class="htmlview paragraph">This paper presents a comparative study between many control techniques to investigate the efficiency of the path tracking in various driving scenarios. In this study the Model predictive control (MPC), the adaptive model predictive control (AMPC) and the Stanley controller are employed to ensure that the vehicle follows reference paths accurately and robustly under varying environmental and vehicular conditions. Two driving scenarios are utilized S-road and Curved-road with MATLAB/Simulink under three different vehicle speeds to investigate vehicle performance employing the root mean square error (RMSE) as the performance evaluation function. Particle swarm optimization (PSO) utilized for optimizing the six parameters of the MPC prediction horizon (P), Control horizon(m), manipulated variable rates, manipulated variables weights and two output variables weights. Four objective functions are employed with PSO and compared to each other in terms of the time domain regarding the RMSE of the lateral position and yaw angle to produce an enhanced performance characteristic for S-road. The comparison results shows that the Adaptive-MPC gives reduced RMSE value of yaw angle error compared to MPC at three different speeds for S-Road and Curved-Road. Stanley controller gives reduced RMSE value of lateral position error compared to MPC and Adaptive-MPC at three various speeds for S-Road and Curved-Road. Also, the results of MPC optimized with PSO gives better results compared to MPC for the all four objective functions.</div></div>