Bio-Inspired Optimization Algorithms Applied to the GAPID Control of a Buck Converter

Abstract

Although the proportional integral derivative (PID) is a well-known control technique applied to many applications, it has performance limitations compared to nonlinear controllers. GAPID (Gaussian Adaptive PID) is a non-linear adaptive control technique that achieves considerably better performance by using optimization techniques to determine its nine parameters instead of deterministic methods. GAPID represents a multimodal problem, which opens up the possibility of having several distinct near-optimal solutions, which is a complex task to solve. The objective of this article is to examine the behavior of many optimization algorithms in solving this problem. Then, 10 variations of bio-inspired metaheuristic strategies based on Genetic Algorithms (GA), Differential Evolution (DE), and Particle Swarm Optimization (PSO) are selected to optimize the GAPID control of a Buck DC–DC converter. The computational results reveal that, in general, the variants implemented for PSO and DE presented the highest fitness, ranging from 0.9936 to 0.9947 on average, according to statistical analysis provided by Shapiro–Wilks, Kruskall–Wallis and Dunn–Sidak post-hoc tests, considering 95% of confidence level.