Design of Improved PID Controller Based on PSO-GA Hybrid Optimization Algorithm in Vehicle Lateral Control

Abstract

Considering that it is difficult for a single PID controller to adapt to different vehicle speeds in the context of vehicle path lateral tracking control, this paper proposes a segmented fuzzy PID controller based on particle swarm optimization (PSO) and on the genetic algorithm (GA), namely the hybrid optimization algorithm PCAG. Firstly, the vehicle speed is divided into several intervals, and different PID controller parameters are used for each interval. Secondly, in order to reduce the overshoot and stabilization time, the proposed PCAG algorithm is employed, which is a combination of PSO, particle swarm optimization with convergence factor (PSO-CF), adaptive particle swarm optimization (APSO) and GA. Further on, the PID controller parameters of different speed ranges are adjusted through this algorithm. Finally, in order to make up for the shortcomings of a single PID controller in the context of time-varying vehicle speed control, a fuzzy controller is employed with the purpose of compensating the parameters of the PID controller, so that the controller could adapt to a wider range of vehicle speeds. The simulation results show that the convergence speed and optimization ability of the proposed PCAG are higher than those of PSO. In addition, the segmented fuzzy PID controller optimized by PCAG can adapt to different vehicle speeds and features an excellent path tracking accuracy.

A Novel Simulation-Based Optimization Method for Autonomous Vehicle Path Tracking with Urban Driving Application

“…Zhao et al [17] introa genetic algorithm (GA)-based PID controller for trajectory tracking in self-driving s. Utilizing a Fractional Order PID (FOPID) controller, Al-Mayyahi et al [18] fon controlling a non-holonomic autonomous ground vehicle's movement to follow fic reference path, with its parameters refined using particle swarm optimization A soft computing evolutionary method, specifically the GA, was applied by Abajo 9] to find the optimal PID regulator settings for varying routes. Gao et al [20] proa segmented fuzzy PID controller enhanced by a hybrid PCAG algorithm that comdaptive PSO and GA. Hajjami et al [21] utilized the butterfly optimization algo-BOA) to establish an optimal PID controller for improving the lateral dynamics of ucif et al [22] determined the optimal parameters for a robot manipulator controlemploying an innovative evolutionary algorithm, namely the whale optimization hm (WOA). Ma'ani and Nazaruddin [23] designed a longitudinal controller applyflower pollination algorithm (FPA) for controller parameter optimization.…”

“…A soft computing evolutionary method, specifically the GA, was applied by Abajo et al [19] to find the optimal PID regulator settings for varying routes. Gao et al [20] proposed a segmented fuzzy PID controller enhanced by a hybrid PCAG algorithm that combines adaptive PSO and GA. Hajjami et al [21] utilized the butterfly optimization algorithm (BOA) to establish an optimal PID controller for improving the lateral dynamics of AVs. Loucif et al [22] determined the optimal parameters for a robot manipulator controller by employing an innovative evolutionary algorithm, namely the whale optimization algorithm (WOA).…”