Brushless DC motor speed control system employing fuzzy neural network PID base on the optimized whale algorithm

The classical PID controller, which serves for controlling the revolutions per minute of brushless direct current motor (BLDCM), has limitations of long settle time, slow response speed and violent fluctuation. To remedy this matter occurred above, by virtue of the whale optimization algorithm WOA and the fuzzy neural network PID controller modeled on the elementary structure of BLDCM, a modified approach to adjust revolutions per minute is raised in our paper. At the outset, under the action of the nonlinear approximation of fuzzy neural network, the uncertain coefficients of PID controller are timely altered. Then, considering that the initial values of fuzzy neural network are stochastic, the WOA method is used to prepare the parameters for neural network and it is further refined via the Lévy flight perturbation method. Eventually, there are several simulations to test this controller, and results demonstrate that the enhanced controller put forward by us is able to have good effects on the properties of system accuracy, response speed and anti-disturbance capability.