Applying Particle Swarm Optimization to Refine PID Controllers for Second-Order Linear Systems

Abstract

This research proposes a novel approach for designing a proportional-integral-derivative (PID) controller for a class of second-order linear systems. The method employs particle swarm optimization implemented in MATLAB to optimize the PID controller parameters based on time domain specifications. Particle Swarm Optimization (PSO) has emerged as a valuable tool for the fine-tuning of PID controllers, complemented by the formulation of appropriate fitness and constraint functions. This methodology facilitates the automatic and efficient adjustment of PID controller parameters, a crucial factor in achieving optimal control performance across diverse systems. In our study, we showcase the implementation of PSO and its profound impact on enhancing controller performance. This enhancement is characterized by improved system stability, minimized overshoot, and swifter response times. Emphasizing the pivotal role of the fitness function, we demonstrate its significance in steering the optimization process by quantifying system performance, ensuring the alignment of controller parameters with predefined control objectives.