Optimization of PID Controller Tuning method using Evolutionary Algorithms

PID controllers are vital in the control for many systems such as suspension systems, Automatic Voltage Regulators (AVR), Self-Stabilizing Systems as well as DC motor speed control. Proper tuning of the PID controller will ensure desired performance is achieved with any application. In this study, the proportional-integral-derivative (PID) controller will be tuned with several methods, including heuristic methods such as Trial and Error (T&E), Ziegler Nichols (ZN) as well as Evolutionary Algorithms such as the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO) for DC Motor speed control. This paper will first determine the most optimized form of the Evolutionary Algorithms such as GA and PSO by determining the optimized parameters for both algorithms. Comparison between all tuning techniques will then be conducted. The performance between each method will be quantified by analyzing the transient response parameters such as Overshoot, Settling Time, Rising Time, and Steady State Error. From the simulations, the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are superior compared to the Trial and Error (TE) and the Ziegler Nichols method (ZN). However, the Particle Swarm Optimization (PSO) performed better in optimizing the PID constants at a faster rate compared to Genetic Algorithm (GA).