Adaptive techniques optimized by bio-inspired algorithm for the control of a BLDC motor

Abstract

Long operating life, high dynamic response and efficiency, and higher torque-to-weight ratio make the BLDC motor attractive for several applications, such as electric vehicles, drones, etc. Due to its versatility, the BLDC motor can be exposed to applications in which load disturbances, sudden disturbances, and parameter variation occur, making conventional control techniques such as proportional-integral-derivative PID controller not reach its variables with precision and agility. To avoid this inconvenience, the PID controller can improve its performance when used in conjunction with adaptive techniques that collect data from the system’s operating environment and perform adjustments based on the condition it is in, dynamically minimizing system failures.Adaptive techniques based on fuzzy logic or Gaussian functions can be an alternative to this impasse. However, the choice of parameters and variables empirically are some obstacles that the designer faces, requiring full knowledge of the system’s behavior to which they will be applied. For this, using a metaheuristic optimization algorithm such as Particle Swarm Optimization (PSO) would be a solution, this bio-inspired algorithm searches in a complex universe of multiple solutions the best one for a given problem. This paper aims to compare different control techniques, such as PID, Hybrid Fuzzy-PID Hybrid Fuzzy-PID, and GAPID optimized by PSO for speed control of a BLDC motor, through simulations performed in the Simulink software and its practical implementation in an ESP32 microcontroller.