Metaheuristic-based optimization applied to GAPID controller

Abstract

This work presents a comparative study of the performance of two bioinspired metaheuristics - Whale Optimization Algorithm (WOA) and Artificial Bee Colony (ABC) to find the parameters of an Adaptive Gaussian PID controller (GAPID), comparing the results with a known benchmark, the Particle Swarm Optimization (PSO). The GAPID was tested with a Buck step-down converter, and the overall system exhibits multimodal characteristics where different solutions that can achieve similar performances. The GAPID gains are defined to be relative to the PID gains in order to inherit the same design requiremets. This results a total of six parameters to be defined using the optimization algorithms. To determine the quality of the results obtained by the optimizers, evaluation metrics based on the Integral of Absolute Error (IAE) were used to determine the fitness of the solutions found. The computational results were obtained through simulations using julia® (V1.5.3), which follows that all the methods used are capable of solving the proposed problem. In the overall evaluation, the PSO obtained the best results and comparing the two bioinspired metaheuristics, the WOA obtained the best result.