Wavelet neural learning-based type-2 fuzzy PID controller for speed regulation in BLDC motor

In brushless direct current (BLDC) motors, speed control is a prominent operation based on which these motors are widely used in higher-end industrial applications including robotics, aeronautics, disk drives, factory automation, consumer electronics, transport and military applications. A novel wavelet neural learning (WNL)-based type-2 fuzzy system controller is devised in this paper to accomplish effective speed control of the BLDC motor with the set specifications. The performance characteristics of the BLDC motor are simulated and analysed based on the wavelet neural learning-based type-2 fuzzy proportional–integral–derivative (PID) controller. This work employs wavelet neural learning to provide inputs to the constructed new type-2 fuzzy PID controller model, and this drives the BLDC motor mechanism. The gain values of the PID controller are tuned with the developed WNL-based type-2 fuzzy system to carry out most operational speed regulation process for the motor mechanism. The developed speed controller model for the BLDC motor is tested for its superiority by performing step variations of the input signal and also for load disturbances. In WNL, to derive the output a wavelet function is applied and this drives the operation of the type-2 fuzzy system PID controller and attains better gain values for speed control operation. The classic slime mould algorithm is used in this contribution to tune the weight coefficients during wavelet neural learning process and due to which the delayed convergence is controlled. Results attained during the simulation process prove the effectiveness and superiority of the proposed speed controller for the design specifications of the BLDC motor in comparison with the earlier literature contributions made.