COMPARISON OF SEVERAL GENETIC ALGORITHM STRATEGIES ON A NONLINEAR GAPID CONTROLLER OPTIMIZATION APPLIED TO A BUCK CONVERTER
This work presents a comparison of six versions of the Genetic Algorithm to optimize the parameters of the nonlinear GAPID controller (Gaussian Adaptive Proportional, Integral and Derivative), elaborated to control a step-down DC-DC converter. This task comprises 8 free parameters and there is no analytic solution to solve it. Also, the design of the controller is hard to determine because there can exist several near-optimal solutions with dierent values for the parameters, which denes this problem as multimodal. In this sense, diferent optimization strategies can lead to dierent solutions. This paper analyzes the behavior of six distinct Genetic Algorithm strategies and compares the obtained results.