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.
Segmented PID, Fuzzy control, Particle swarm optimization, Genetic algorithm, PCAG.
Shenqi GAO, Song GAO, Weigang PAN, Mushu WANG, "Design of Improved PID Controller Based on PSO-GA Hybrid Optimization Algorithm in Vehicle Lateral Control", Studies in Informatics and Control, ISSN 1220-1766, vol. 30(4), pp. 55-65, 2021. https://doi.org/10.24846/v30i4y202105
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