Speed control of BLDC motor with neural controller

Lokesh Kumar Agrawal; Bhavesh Kumar Chauhan; Nitin Kumar Saxena; Puneet Joshi

doi:10.17485/IJST/v14i4.2164

Article

Speed control of BLDC motor with neural controller

VIEWS 3126
PDF 2369

Indian Journal of Science and Technology

DOI: 10.17485/IJST/v14i4.2164

Year: 2021, Volume: 14, Issue: 4, Pages: 373-381

Original Article

Speed control of BLDC motor with neural controller

Lokesh Kumar Agrawal^1*, Bhavesh Kumar Chauhan², Nitin Kumar Saxena³,Puneet Joshi⁴

¹Research Scholar, Department of Electrical Engineering, IFTM University, Moradabad, India
²Professor, Department of Electrical Engineering, BBDNITM, Lucknow, India
³Associate Professor, Department of Electrical & Electronics Engineering, KIET Group of Institutions, Ghaziabad, India
⁴Assistant Professor, Department of Electrical Engineering, REC, Ambedkarnagar, India

*Corresponding Author
Email: [email protected]

Received Date:01 December 2020, Accepted Date:20 January 2021, Published Date:02 February 2021

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objective: BLDC motor specifically used for high speed applications and needs sophisticated speed control. Conventional controls techniques require more physical involvement for assisting BLDC motor to operate with precise speed value. However, soft tuning based controller may give comparatively better results for that and the same has been elaborated in present paper using neural controller of speed as per requirements.the main objective of this study may be summarized as (i) MATLAB simulink model for BLDC motor incorporated with soft tuned controller and inverter simultaneously, (ii) development of neural controller for BLDC motor control, and (iii) validation of results developed through proposed controller with the available results using PI, PID and fuzzy logic controller. Methods: The speed obtained through simulink model with input supply voltage variations are used as a reference signal for the training and testing purpose of neural controller. With the help of these reference signals, neural control executes and gives optimum values of external parameters such as speed and electromagnetic torque. Result: The study outcome provides dynamic execution of proposed controllerbased on neural network. The proposed controller’s results are validated with the reference results obtained mathematically for the system. The controller efficiency and sensitivity has been checked by MATLAB-Simulink software. Novelty: comparative study for mathematically obtained reference values and neural controller based results are proposed in this paper. The results are validated with the help of Matlab produces tables and figures.

Keywords: BLDC Motor; speed control; neural network controller

References

Gobinath S, Madheswaran M. Deep perceptron neural network with fuzzy PID controller for speed control and stability analysis of BLDC motor. Soft Computing. 2020;24(13):10161–10180. Available from: https://dx.doi.org/10.1007/s00500-019-04532-z
Tibor B, VF, Durovsky F. Modeling and Simulation of the BLDC Motor in MATLAB GUI. In: Proceedings of the IEEE Fifth International conference on Fuzzy Systems and Knowledge Discovery. (pp. 1403-1407) 2011.
Ho TY, Chen YJ, Chen PH, Hu PC. The Design of a Motor Drive Based on Neural Network. IEEE International Conference on Applied System Innovation (ICASI). 2017;p. 337–340. Available from: https://doi.org/10.1109/ICASI.2017.7988421
Hendershot JR, Miller TJE. Design of brushless Permanent-magnet Motors. Oxford, UK. Oxford Science. 1994.
Agrawal LK, Chauhan BK, Banerjee GK. Speed Control of Brushless DC Motor Using Fuzzy Controller”. International Journal of Pure and Applied Mathematics. 2018;119(15):2689–2696. Available from: http://www.acadpubl.eu/hub/
Mamadapur A, Mahadev DU, G. Speed Control of BLDC Motor Using Neural Network Controller and PID Controller. In: 2nd International Conference on Power and Embedded Drive Control (ICPEDC). (pp. 146-151) 2019.
KEH, MM, AEK, Ayad H. Adaptive Control Using Neural Networks and Approximate Models for Nonlinear Dynamic Systems. Modelling and Simulation in Engineering. 2020. Available from: https://doi.org/10.1155/2020/8642915
Agrawal LK, Chauhan BK, Banerjee GK. Speed Control of Brushless DC Motor Using Conventional Controllers”. International Journal of Pure and Applied Mathematics. 2018;119(16):3955–3961. Available from: http://www.acadpubl.eu/hub/
Laxmiprasanna CH, Palakeerthi R. BLDC Drive Control using Artificial Intelligence Technique. International Journal of Computer Applications. 2015;118(4):5–9. Available from: https://dx.doi.org/10.5120/20731-3100
Aris 1RSNAR, ASAG, Zain MLM. Enhancement of Variable Speed Brushless DC Motor using Neural Network”. Indian Journal of Science and Technology. 2016;9(14). Available from: https://doi.org/10.17485/ijst/2016/v9i14/88728
Varalakshmi V, Suseela N, Sundaram TGG, Ezhilarasi S, Indrani B. Statistics Higher Secondary– First Year (First). Tamilnadu Textbook Corporation. 2004.
Kamra N, Goyal P, Seo S, Zois V. Parallel Gradient Descent for Multilayer Feedforward Neural Networks, CSCI-503: Parallel Programming, Fall 2016, Final Project. Final Project. 2016.
Saxena NK, Kumar A. Reactive power control in decentralized hybrid power system with STATCOM using GA, ANN and ANFIS methods. International Journal of Electrical Power & Energy Systems. 2016;83:175–187. Available from: https://dx.doi.org/10.1016/j.ijepes.2016.04.009

Copyright

© 2021 Agrawal et al.This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)