Double-Loop PID Control with Parameter Optimization for an Autonomous Electric Vehicle

An autonomous vehicle is a fascinating and trendy topic from both research and practical perspectives. It can be said that this is a development tendency for future automobiles and attracts the attention and a substantial amount of finance from big automobile corporations worldwide. In terms of technology, driving a driverless car puts a great deal of pressure since there is only one control input (steering angle) that must simultaneously control the position and yaw angle of the vehicle. Moreover, the position of the vehicle over time is a function of the yaw angle. In order to solve the problem, this study proposes a new approach with the use of a dual-loop Proportional Integral Derivative (PID) controller combined with optimizing parameters based on the Particle Swarm Optimization (PSO) algorithm to improve the control performance. By simulating cars following given trajectories based on the car's kinematics and dynamics, the efficiency and feasibility of the work can be assessed. The numerical simulation results of both the established PID controller and PSO algorithm are given at the end of the presentation.