A permanent magnet synchronous motor (PMSM) is one kind of popular motor.
They are utilized in industrial applications because their abilities
included operation at a constant speed, no need for an excitation current,
no rotor losses, and small size. In the following paper, a fuzzy
evolutionary algorithm is combined with a proportional-integral-derivative
(PID) controller to control the speed of a PMSM. In this structure, to
overcome the PMSM challenges, including nonlinear nature, cross-coupling,
air gap flux, and cogging torque in operation, a Takagi-Sugeno fuzzy
logic-PID (TSFL-PID) controller is designed. Additionally, the particle
swarm optimization (PSO) algorithm is developed to optimize the membership
functions' parameters and rule bases of the fuzzy logic PID controller. For
evaluating the proposed controller's performance, the genetic algorithm
(GA), as another evolutionary algorithm, is incorporated into the fuzzy PID
controller. The results of the speed control of PMSM are compared. The
obtained results demonstrate that although both controllers have excellent
performance; however, the PSO based TSFL-PID controller indicates more
superiority.
Speed control of permanent magnet synchronous motor by using fuzzy logic controllers having different inference methods
