Parameter effect analysis of particle swarm optimization algorithm in PID controller design

Abstract

PID controller has still been widely-used in industrial control applications because of its advantages such as functionality, simplicity, applicability, and easy of use. To obtain desired system response in these industrial control applications, parameters of the PID controller should be well tuned by using conventional tuning methods such as Ziegler-Nichols, Cohen-Coon, and Astrom-Hagglund or by means of meta-heuristic optimization algorithms which consider a fitness function including various parameters such as overshoot, settling time, or steady-state error during the optimization process. Particle swarm optimization (PSO) algorithm is often used to tune parameters of PID controller, and studies explaining the parameter tuning process of the PID controller are available in the literature. In this study, effects of PSO algorithm parameters, i.e. inertia weight, acceleration factors, and population size, on parameter tuning process of a PID controller for a second-order process plus delay-time (SOPDT) model are analyzed. To demonstrate these effects, control of a SOPDT model is performed by the tuned controller and system response, transient response characteristics, steady-state error, and error-based performance metrics obtained from system response are provided.