PID Controller Parameter Optimization Based on Improved Particle Swarm Optimization Algorithm

Abstract

In today's industrial production process, PID controllers are widely used, but they are still difficult to obtain a set of PID parameters with excellent control performance. In order to solve this problem, an improved particle swarm optimization algorithm based on the experience of each particle in the population is proposed, and a backtracking factor is introduced to avoid particles falling into premature phenomenon. The algorithm adjusts the inertia weight factor according to the relative advantages and disadvantages of each particle in the population, so that each particle can make a more appropriate optimization strategy. Four classical test functions are used to prove the superiority of the algorithm. And taking the standard third-order delay model as an example, through comparing with other improved particle swarm algorithm, it can be seen that the algorithm proposed in this paper has better effect on PID controller parameter optimization.