Adaptive Neuro-Fuzzy Inference System based speed controller for brushless DC motor

• An efficient ANFIS based speed controller is proposed for brushless DC motor.
• ANFIS controller is modeled through modified data of the Fuzzy Tuned PID controller.
• Off-line Hybrid Learning Algorithm is applied to identify the ANFIS parameters.
• Simulation has been performed and analyzed for varying speed and load conditions.

In this paper, a novel controller for brushless DC (BLDC) motor has been presented. The proposed controller is based on Adaptive Neuro-Fuzzy Inference System (ANFIS) and the rigorous analysis through simulation is performed using simulink tool box in MATLAB environment. The performance of the motor with proposed ANFIS controller is analyzed and compared with classical Proportional Integral (PI) controller, Fuzzy Tuned PID controller and Fuzzy Variable Structure controller. The dynamic characteristics of the brushless DC motor is observed and analyzed using the developed MATLAB/simulink model. Control system response parameters such as overshoot, undershoot, rise time, recovery time and steady state error are measured and compared for the above controllers. In order to validate the performance of the proposed controller under realistic working environment, simulation result has been obtained and analyzed for varying load and varying set speed conditions.