PID Controller Tuning for an AVR System Using Particle Swarm Optimisation Techniques and Genetic Algorithm Techniques: A Comparison Based Approach

This paper discusses tuning a Proportional-Integral-Derivative (PID) controller for an Automatic Voltage Regulator (AVR) system utilizing a particle swarm optimization technique and genetic algorithm. The primary objective is to compare the two methods. The AVR system was modeled and simulated using MATLAB, and the performance of the optimized PID controller was analyzed. The results demonstrate significant improvements in system performance with the metaheuristic-tuned PID controllers. Specifically, the GA-tuned PID controller achieved the best overshoot reduction (0.8%) and steady-state error minimization (0.0005), making it highly suitable for applications requiring precise voltage control. On the other hand, the PSO-tuned PID controller excelled in reducing settling time (2.7 seconds) and improving rise time (1.2 seconds), making it ideal for systems requiring rapid stabilization. Both metaheuristic approaches showed substantial enhancements. The study highlights the importance of selecting the appropriate optimization technique based on specific system requirements, whether the priority is minimizing overshoot, reducing settling time, or achieving near-zero steady-state error