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Friday, 2 March 2018

Adaptive Speed Control of Brushless DC (BLDC) Motor Based on Interval Type-2 Fuzzy Logic

To precisely control the speed of BLDC motors at high speed and with very good performance, an accurate motor model is required. As a result, the controller design can play an important role in the effectiveness of the system. The classic controllers such as PID are widely used in the BLDC motor controllers, but they are not appropriate due to non-linear model of the BLDC motor. To enhance the performance and speed of response, many studies were taken to improve the adjusting methods of PID controller gains by using fuzzy logic. Use of fuzzy logic considering approximately interpretation of the observations and determination of the approximate commands, provides a good platform for designing intelligent robust controller. Nowadays type-2 fuzzy logic is used because of more ability to model and reduce uncertainty effects in rule-based fuzzy systems. In this paper, an interval type-2 fuzzy logic-based proportional-integral-derivative controller (IT2FLPIDC) is proposed for speed control of brushless DC (BLDC) motor. The proposed controller performance is compared with the conventional PID and type-1 fuzzy logic-based PID controllers, respectively in MATLAB/Simulink environment. Simulation results show the superior IT2FLPIDC performance than two other ones.


1.      Brushless DC (BLDC) Motor
2.      Invertal Type-2 Fuzzy Logic
3.      Speed Control
4.      Self-tuning PID Controller


Figure 1. Block Diagram of speed control of BLDC Motor

Figure 2. Speed Deviation of BLDC Motor

Figure 3. Load Deviation of BLDC Motor

Figure 4. Torque Deviation of BLDC Motor

In this paper, the speed control of the BLDC motor is studied and simulated in MATLAB/Simulink. In order to overcome uncertainties and variant working condition, the adjustment of PID gains through fuzzy logic is proposed. In this study, three controller types are considered and compared: conventional PID, type-1 and type-2 fuzzy-based self-tuning PID controllers. The simulation results show that type-2 fuzzy PID controller has superior performance and response than two other ones.
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