Particle Swarm Optimization Algorithm Based on Natural Selection and Simulated Annealing for PID Controller Parameters

Abstract

The values of a PID controller’s parameters determine the controller’s effect. The particle swarm optimization (PSO) algorithm is often used to optimize the controller’s parameters. However, PSO has some inherent defects, such as premature convergence and easily turning into a local optimization. In this paper, an improved particle swarm optimization algorithm based on a natural selection strategy and a simulated annealing mechanism is proposed to optimize the PID controller’s parameters. In the improved PSO algorithm, the natural selection strategy is used to accelerate the rate of convergence, and the simulated annealing mechanism is employed to ensure the accuracy of the search and increase its ability to avoid local optima. The improved algorithm not only guarantees the convergence speed but also has a better ability to jump out of the local optimum trap. To verify the performance of the improved algorithm, four types of algorithms are selected to optimize the PID controller parameters of the Second-order Time-delayed System and the Permanent Magnet Synchronous Motor (PMSM) Servo System. They are the PSO algorithm, the optimization algorithm proposed in this paper (NAPSO), the seeker optimization algorithm (SOA), and the genetic algorithm (GA). The results show that the improved algorithm has a better optimal solution.