Autonomous vehicles represent a central component of future automotive technologies. One of the critical challenges in enabling this technology is the development of path following control systems. This study presents a linear quadratic integral (LQI) optimal control approach improved by particle swarm optimization (PSO), reinforcement learning particle swarm optimization policy (PSO-P), and pure pursuit (PP) for the design of a control system for autonomous vehicle path following. A novel combined control system is proposed by integrating LQI, PSO-P, and PP methods. To further enhance system performance, a feed-forward steering control system is incorporated into all control strategies. The proposed and benchmark control systems are evaluated and compared using time-domain simulations in the MATLAB/Simulink and IPG CarMaker co-simulation environment. Statistical analyses were performed using performance metrics and data from two distinct path curvatures. The results demonstrated the efficacy of the method compared to alternative control strategies.
Sevim et al. (Wed,) studied this question.