A New Self-Tuning Nonlinear Model Predictive Controller for Autonomous Vehicles

Abstract

Autonomous driving has recently been in considerable progress, and many algorithms have been suggested to control the motions of driverless cars. The model predictive controller (MPC) is one of the efficient approaches by which the speed and direction of the near future of an automobile could be predicted and controlled. Even though the MPC is of enormous benefit, the performance (minimum tracking error) of such a controller strictly depends on the appropriate tuning of its parameters. This paper applies the particle swarm optimization (PSO) algorithm to find the global minimum tracking error by tuning the controller’s parameters and ultimately calculating the front steering angle and directed motor force to the wheels of an autonomous vehicle (AV). This article consists of acquiring vehicle dynamics, extended model predictive control, and optimization paradigm. The proposed approach is compared with previous research in the literature and simulation results show higher performance, and also it is less computationally expensive. The simulation results show that the proposed method with only three adjustable parameters has an overshoot of about 8% and its RMSE is 0.72.