Speed Characteristics of Brushless DC Motor Using Adaptive Neuro Fuzzy PID Controller under Different Load Condition
S. Swapna1, K. Siddappa Naidu2
1S. Swapna, Research Scholar, Department of Electrical and Electronics Engineering, Vel Tech Rangarajan Dr. Sangunthala R&D Institute of Science & Technology, Chennai (Tamil Nadu), India.
2K. Siddappa Naidu, Professor, Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sangunthala R&D Institute of Science & Technology, Chennai (Tamil Nadu), India.
Manuscript received on 24 April 2019 | Revised Manuscript received on 02 May 2019 | Manuscript Published on 08 May 2019 | PP: 472-479 | Volume-7 Issue-5S3 February 2019 | Retrieval Number: E11840275S19/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The increasing development towards usage of accurately controlled, high starting torque, high efficiency and low noise motors for devoted applications has fascinated the attention of researcher in permanent magnet brushless direct current (PMBLDC) motor. BLDC motors can act as suitable option to the conventional motors like permanent magnet direct current motor (PMDC), Switched Reluctance Motor (SRM) etc. This research paper analysis and compares the performance of a BLDC motor supplying various types of loads, and at the same time, implementing different control techniques such as fuzzy PID and ANFIS PID (Adaptive Neuro-Fuzzy Inference System PID). A comparison has been made in this research paper by observing the various speed response of brushless direct current motor at the time of application of load as well as at the time of removal of the load. The efficiency of the proposed method such as Adaptive Neuro-Fuzzy Inference System has been verified in terms of rise time, settling time and peak overshoot by developing the simulation model using MATLAB/SIMULINK.
Keywords: BLDC motor; Fuzzy Proportional-Integral-Derivative (Fuzzy PID) Controller; Adaptive Neuro-Fuzzy Inference System PID (ANFIS PID) Controller; MATLAB/SIMULINK.
Scope of the Article: Fuzzy Logics