Gaussian Adaptive PID control (GAPID) and the Fuzzy logic PID control (FLPID) Tuned by Particle Swarm Optimization for a speed control in a BLDC motor

Resumo

The usage of Brushless Direct Current Electric Motors (BLDC) is each time more frequent in indus-
trial appliances such as the automobile segment. In such application the BLDC motor is exposed to many types

of charge disturbances which makes the conventional control methods, such as proportional–integral–derivative
controller (PID), not reaching its variables with precision in cases of sudden perturbation and variation of the

parameters. Thus, the PID controller might have its performance improved with the application of adaptive tech-
niques which collect data from the operating system environment and make adjusts based in the condition where

it is found. In this case, two techniques are chosen: the Gaussian Adaptive PID Control (GAPID) and the Fuzzy
logic PID Control (FLPID), choosing its parameters is not an easy task, although they can be obtained through the
usage of optimization techniques, such as the Particle Swarm Optimization (PSO), ensuring a better performance
and robustness of the GAPID and FLPID compared to the linear PID by load and gain sweep tests, achieving
fast response and minimal variation. This paper aims to accomplish the comparison between different control
techniques for the speed control in a BLDC motor and its practical implementation in a ESP32 microcontroller.