Optimum Online Controller Tuning Through DE and PSO Algorithms: Comparative Study with BLDC Motor and Offline Controller Tuning Strategy

This work presents the online controller tuning, through metaheuristic algorithms, for a Proportional Integral controller of a Brushless Direct Current (BLDC) motor via the indirect adaptive tuning approach. The tuning is an optimization problem solved through Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms in their online versions. The functionals of the optimization problem aim to minimize the Integrated Absolute Error (IAE) about speed regulation and reduce the Average Power required by the motor. A set of experiments considering different disturbances applied to the motor is performed, and a comparison between the optimal online and the optimal offline approaches is carried out. Finally, the results show that the online approach improves speed regulation and reduces the motor's energy consumption.