Improvement of Particle Swarm Optimization and its Application for Optimization of PID Parameters on FRIT

Abstract

In PID control, it is important to properly adjust the control parameters, and one of the tuning methods is FRIT (Fictitious Reference Iterative Tuning) (Soma et al., 2004). FRIT is used in actual control because it can appropriately adjust the control parameters based on the results of one control experiment. Adjusting control parameter problems are multidimensional nonlinear optimization problem, and the genetic algorithms (GA) are used to determine the contorol parameters in FRIT. Azuma and Watanabe (2014) and Murakami et al. (2018) have improved particle swarm optimization (PSO) to improve solution search performance for FRIT, and they have showed that higher control performance can be obtained than the parameter determination method using GA. This paper tries to improve PSO to improve the performance of control parameter adjustment in FRIT using PSO.