Optimal PID Parameters Tunning for a DC-DC Boost Converter: A Performance Comparative Using Grey Wolf Optimizer, Particle Swarm Optimization and Genetic Algorithms

Abstract

The Grey Wolf Optimizer (GWO) algorithm is a metaheuristic optimization method based on the hunting made by wolves in nature. In this work, the GWO algorithm was proposed for tuning a Proportional-Integral-Derivative (PID) controller parameters for a DC-DC boost converter. DC-DC boost converters are electronic devices widely used for voltage regulation in renewable energies applications, these devices need a controller, commonly a PID controller which needs to be correctly tuned to reduce the error between the reference voltage and the system output voltage. Classical PID controller tuning methods require a mathematical formulation or an empirical system response analysis, bioinspired optimization algorithms are an alternative for system design. This paper presents a performance comparative analysis between the proposed GWO algorithm, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The simulation was carried out using MATLAB/Simulink environment, then the tuned PID controller performance was evaluated using the system response analysis to variable load conditions and Root Mean Squared Error (RMSE) between reference and output voltage. Results showed that the proposed GWO algorithm has a lower RMSE compared to PSO and GA, and therefore, it could be an effective method for optimal PID controllers for power converters applications.