An adaptive control study for a DC motor using meta-heuristic algorithms

In this work, a comparative study of the use of different meta-heuristic techniques in the adaptive control for the speed regulation of the DC motor with parameters uncertainties is presented. Several adaptive controllers based on the optimizers of Differential Evolution (DE), Particle Swarm Optimization (PSO), Bat Algorithm (BAT), Firefly Algorithm (FFA) and Wolf Search Algorithm (WSA) are proposed in order to on-line tune the parameters of the DC motor. These parameters are used in calculating the control signal. Simulations show the efficacy of each control strategy. Given the results, the controller based on PSO is one of the most promising alternatives for this approach.