114 Int. J. Industrial Electronics and Drives, Vol. 4, No. 2, 2018 Copyright © 2018 Inderscience Enterprises Ltd. Design of model reference adaptive controller-based speed control of brushless DC motor drive Ramasamy Latha* and Palanisamy Vishnu Kumaran Department of Electrical and Electronics Engineering, PSG College of Technology, Peelamedu, Coimbatore – 641004, Tamilnadu, India Email: rla.eee@psgtech.ac.in Email: pvishnukumaran@gmail.com *Corresponding author Abstract: The BLDC motors are used in automatic systems for trajectory tracking, motion control, etc. If any external disturbances and set point variations occurs, conventional controllers are unable to track the set point accurately. Hence, adaptive controllers are proposed to overcome this issue. The paper focuses on the development of model reference adaptive controller (MRAC) technique for speed control of BLDC motor. The MRAC approach is implemented with the conventional PID controller. The control strategy is simulated and investigated using MATLAB/Simulink. Simulation results of the PID, fuzzy-PID and MRAC-PID have been presented and the performances of the controllers are analysed on the basis of various control system parameters such rise time, settling time and peak overshoot. The results demonstrate that the proposed controller gives a good dynamic behaviour, perfect speed tracking with minimum overshoot. The developed controller is implemented in real-time using DSP processor and the respective PWM pulses are acquired for different ranges of speed. Experimental results prove the effectiveness of the proposed control approach. Keywords: brushless DC motor; proportional-integral-derivative; PID; model reference adaptive control; MRAC. Reference to this paper should be made as follows: Latha, R. and Vishnu Kumaran, P. (2018) ‘Design of model reference adaptive controller-based speed control of brushless DC motor drive’, Int. J. Industrial Electronics and Drives, Vol. 4, No. 2, pp.114–119. Biographical notes: Ramasamy Latha received her BE in Electrical and Electronics Engineering from the Bharathiyar University in 2001. She received her ME in Power Systems Engineering from the Anna University in 2005 and PhD in Electrical Engineering from the same university in 2014. She started working for PSG College of Technology since 2007 as a Lecturer. Currently, she works as an Associate Professor in the Department of Electrical and Electronics Engineering and her areas of interest include microgrids, renewable energy conversion systems and power electronic drives and controllers. Palanisamy Vishnu Kumaran received his BE in Electrical and Electronics Engineering from the Anna University in 2014 and ME Control Systems from the same university in 2016. His current research interests are electrical machines, power electronics drives and controllers. 1 Introduction In recent years brushless DC motors (BLDCs) are widely used in industries like medical, mechanical, automobile, electrical and avionics due to its flexible characteristics. Conventional controllers like proportional (P), proportional integral (PI) and proportional-integral-derivative (PID) do not yield better control characteristics. The performance of fuzzy and PID controller-based BLDC servomotor drive is investigated under different operating conditions such as change in reference speed, parameter variations and load disturbances (Shanmugasundram et al., 2014). The development and performance analysis of model reference adaptive controller (MRAC) using artificial neural network (ANN) for BLDC drive is proposed to solve the problems of nonlinearity, parameter variations and load excursions that occur in BLDC motor drive systems. Real-time experimental results are also verified using TMS320LF2407A (Leena and Shanmugasundaram, 2014). The high power BLDC closed-loop control system for speed detection circuit is designed to improve the performance of motor (Wang, 2012). The performance of two different control techniques such as self-tuning fuzzy PID controller and MRAC is compared with PID compensator (Adel and Shamseldin, 2016). Novel speed estimation approach with control system based on MRAC is presented for low cost BLDC drives with low-resolution hall sensors (Sunil and Rajasekhar, 2014). Fuzzy logic model reference adaptive