AI Summary & Key Takeaways
AI InsightTo demonstrate these effects, control of aSOPDT model is performed by the tuned controller and system response, tran-sient response characteristics, steady-state error, and error-based performancemetrics obtained from system response are provided.
📌 3 Key Takeaways
Abstract
PID controller has still been widely-used in industrial control applications be-cause of its advantages such as functionality, simplicity, applicability, and easyof use. To obtain desired system response in these industrial control appli-cations, parameters of the PID controller should be well tuned by using con-ventional tuning methods such as Ziegler-Nichols, Cohen-Coon, and Astrom-Hagglund or by means of meta-heuristic optimization algorithms which con-sider a fitness function including various parameters such as overshoot, settlingtime, or steady-state error during the optimization process. Particle swarm op-timization (PSO) algorithm is often used to tune parameters of PID controller,and studies explaining the parameter tuning process of the PID controller areavailable in the literature. In this study, effects of PSO algorithm parame-ters, i.e. inertia weight, acceleration factors, and population size, on param-eter tuning process of a PID controller for a second-order process plus delay-time (SOPDT) model are analyzed. To demonstrate these effects, control of aSOPDT model is performed by the tuned controller and system response, tran-sient response characteristics, steady-state error, and error-based performancemetrics obtained from system response are provided.