An adaptive intelligent sliding mode control method for BLDC motor using optimized fuzzy PID controller [articol]

Abstract

Brushless DC motor is one type of permanent synchronous motor, which is applied in various industrial and commercial applications. This is because it has number of advantages like high power density, large torque, lifetime, good speed regulation and reliability. However, the controls of effective speed control and current control of brushless DC motor are still difficult. Further, the uncertainty and non-linear characteristics of the motor system degrades the efficiency of controllers. To address and end this difficulty, a novel control scheme is proposed by using optimized tuned parameters with Fuzzy PID controller for the speed control of BLDC motor. Several nature-inspired optimization algorithms like particle swarm and cuckoo search algorithms are developed for controller design. The proposed Fuzzy PID controller design is carried patterned using cuckoo search algorithm for tuning the optimized parameters. The speed of the motor can be tuned depending upon the sliding mode surface parameters. Similarly, Adaptive Intelligent Sliding Mode Controller is designed by using cuckoo search algorithm which employs an inner loop for current control and outer loop as speed control. Here, two cascaded sliding mode controllers taking into account which improves dynamic control of BLDC motor. It is guaranteed that the developed AISMC strategy deals with uncertainty and non-linear characteristics of unknown external disturbance. The precise experimentation was implemented in MATLAB simulation environment, and system performance is analyzed regarding stator current, torque, back EMF, the line to line voltage and settling time of the motor.