Swarm-Inspired Algorithms to Optimize a Nonlinear Gaussian Adaptive PID Controller

This work deals with metaheuristic optimization algorithms to derive the best parameters for Gaussian Adaptive PID controller. controller represents a multimodal problem, where several distinct solutions can achieve similar performances, and metaheuristics behave differently during process. Finding correct proportionality between is an arduous task that often does not have algebraic solution. The functions of each control action three parameters, resulting in total nine be defined. In this work, we investigate bio-inspired methods dealing problem: Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC) algorithm, Whale Algorithm (WOA). computational results considering Buck converter resistive nonlinear load as case study demonstrated were capable solving task. are presented compared, PSO achieved results.