Sensorless Brushless DC Motor Control Using Type-2 Fuzzy Logic

Keywords: Type-2 Fuzzy Logic Controller, BLDC Motor, Sensorless Control, Type-1 Fuzzy Logic Controller, PID Controller


BLDC motors have many advantages such as a better speed versus torque characteristics, high dynamic response, high efficiency and reliability, low cost drives, long operating life (no brush erosion), noiseless operation, higher speed ranges, and reduction of electromagnetic interference. For this reason, it is used in many different fields today. A BLDC motor requires an inverter and a sensor to achieve commutation process. However, the hall sensor presents quite a few disadvantages from the standpoint of drive’s cost, machine size, temperature sensitivity requiring special arrangements and noise protection. As a result, with the increasing power of embedded computing in recent years sensorless control techniques have been developed and widely used. Additionly, conventional controllers do not give the desired results as these motors are non-linear in nature. Many techniques for BLDC motor speed control have been developed such as PID, Fuzzy logic controller, adaptive neuro fuzzy inference system. But the responses obtained were oscillatory and when a load was applied to these systems, the system's responses were much lower than the reference value. To remove oscillations and achieve a better performance, some new techniques were required. Due to their ability to handle uncertainty with robust and adaptive structure against complex systems, type-2 fuzzy logic systems, which is one of the artificial intelligence techniques started to use in recently years. In this study, the brushless motor was used as without sensor with back emf technique and zero crossing detection. Type-2 fuzzy logic controller was used to better resolve the uncertainties in the system. Simulation performed in Matlab – Simulink and PID, type-1, type-2 fuzzy logic controller results were compared and as a result, it was observed that type-2 fuzzy logic controller gave the most suitable system response to reference value.


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How to Cite
C. Buyukyildiz and I. Saritas, “Sensorless Brushless DC Motor Control Using Type-2 Fuzzy Logic”, IJISAE, vol. 8, no. 4, pp. 184-190, Dec. 2020.
Research Article