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Design of Efficient Adaptive Neuro-Fuzzy Controller Based on Supervisory Learning Capable for Speed and Torque Control of BLDC Motor

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Warianty tytułu
PL
Projekt skutecznego sterownika silnika bezszczotkowego DC z wykorzystaniem sieci neuronowych i logiki rozmytej
Języki publikacji
EN
Abstrakty
EN
Brushless DC (BLDC) motors have been widely used in many field of drives for their high power/weight, high torque, high efficiency, long operating life, noiseless operation, high speed ranges and ease of control. In this paper, a Neuro-Fuzzy Controller (NFC) based on supervisory learning is presented for the speed and torque control of BLDC motors to enhance high control performance of the drive under transient and steady state conditions. This designed controller is combination of Neural Networks (NNs) and Fuzzy Logic (FL), therefore has parallel processing and learning abilities of NNs and inference capacity of FL. For improvement the performance of leaning algorithm and thereupon increase efficiency of drive, instead of usual Error Back Propagation (EBP) learning technique, a fuzzy based supervisory learning algorithm is employed. The proposed controller has simple structure and also due to its modest fuzzy rule in rule-base is relatively easy for implementation. This controller has high accuracy, suitable performance, high robustness and high tracking efficiency. In order to demonstrate the NFC ability to tracking reference speed and torque and also test of robustness and rejection ability of controller against undesired disturbances or suddenly changes in speed and torque, these designs are simulated with MATLAB/SIMULINK. In some cases, results are compared with that of a conventional PID controller and other designs.
PL
W artykule zaprezentowane układ sterowania bezszczotkowym silnikiem DC z wykorzystaniem sterownika Neuro-Fuzzy. Dla poprawienia efektywności uczenia sieci zamiast wstecznej propagacji błędu zaproponowano algorytm wykorzystujący logikę rozmytą. Sterownik okazał się być dokładny, odporny i o dużej efektywności śledzenia zmian. Porównano możliwości kontrolera z konwencjonalnym sterownikiem PID.
Rocznik
Strony
238--246
Opis fizyczny
Bibliogr. 21 poz., rys., tab., wykr.
Twórcy
autor
autor
  • Electronic Engineering from Department of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran, M_Mosavi@iust.ac.ir
Bibliografia
  • [1] H. Lu, L. Zhang and W. Qu, A New Torque Control Method for Torque Ripple Minimization of BLDC Motors With Un-Ideal Back-EMF, IEEE Transaction on Power Electronics, Vol.23, No.2, pp.950-958, March 2008.
  • [2] S. Rajagopalan, Detection of Rotor and Load Faults in Brushless DC Motors Operating under Stationary and Nonstationary Condition, Dr Thesis, Georgia Institute of Technology, August 2006.
  • [3] N. B. Rahman, Neural Network Controller for DC Motor Using MATLAB Application, BS Thesis, University of Malaysia Pahang, November 2008.
  • [4] S. Jung and S. S. Kim, Hardware Implementation of a Real- Time Neural Network Controller With a DSP and a FPGA for Nonlinear Systems, IEEE Transactions on Industrial Electronics, Vol.54, No.1, pp.265-271, February 2007.
  • [5] K. S. Tang, K. F. Man, G. Chen and S. Kwong, An Optimal Fuzzy PID Controller, IEEE Transaction on Industrial Electronics, Vol.48, No.4, pp.757-765, August 2001.
  • [6] A. Rubaai, D. Ricketts and M. D. Kankam, Development and Implementation of an Adaptive Fuzzy-Neural-Network Controller for Brushless Drives, IEEE Transaction on Industry Applications, Vol.38, No.2, pp.441-447, March/April 2002.
  • [7] S. R. Jalluri and B. V. S Ram, A Neuro Fuzzy Controller for Induction Machines Drives, Journal of Theoretical and Applied Information Technology, Vol.19, No.2, September 2010.
  • [8] J. Vieira, F. M. Dias and A. Mot, Neuro-Fuzzy Systems: A Survey, WSEAS Transactions on Systems, Vol.3, No.2, pp.414-419, 2004.
  • [9] D. Nauck, F. Klawon and R. Kruse, Foundations of Neuro- Fuzzy Systems, J. Wiley & Sons, 1997.
  • [10] A. Abraham, Neuro Fuzzy Systems: State-of-the-Art Modeling Techniques, Proceedings of the Sixth International Work Conference on Artificial and Natural Neural Networks, Lecture Notes in Computer Science, Vol.2048, pp.269-276, 2001.
  • [11] L. Rutkowski and K. Cpalka, Flexible Neuro-Fuzzy Systems, IEEE Transaction on Neural Networks, Vol.14, No.3, pp.554- 574, May 2003.
  • [12] K. N. Sujatha, K. Vaisakh and G. Anand, Artificial Intelligence Based Speed Control of Brushless DC Motor, IEEE Conference on Power and General Meeting, pp.1-6, July 2010.
  • [13] J. Cao, B. Cao, P. Xu, S. Zhou, G. Guo and X. Wu, Torque Ripple Control of Position-Sensorless Brushless DC Motor Based on Neural Network Identification, 3rd IEEE Conference on Industrial Electronics and Applications, pp.752-757, June 2008.
  • [14] A. H. Niasar, A. Vahedi and H. Moghbelli, Design and Implementation of Sensor less Control for Four-Switch, Three- Phase Brushless DC Motor Drive Based on DSP Technology, Iranian Journal of Electrical and Computer Engineering, Vol.8, No.1, pp.1-8, Winter-Spring 2009.
  • [15] H. Ji and Z. Li, Design of Neural Network PID Controller Based on Brushless DC Motor, Second International Conference on Intelligent Computation Technology and Automation, Vol.3, pp.46-49, October 2009.
  • [16] J. S. R. Jang, ANFIS: Adaptive-Network-Based Fuzzy Inference System, IEEE Transactions on System, Man, and Cybernetics, Vol.23, No.5, pp.665-685, May 1993.
  • [17] A. H. Niasar, M. A. S. Masoum and H. Moghbeli, Adaptive Neuro-Fuzzy Intelligent Controller via Emotional Learning for Indirect Vector Control (IVC) of Induction Motor Drives, 12th Iranian Conference on Electrical Engineering, 11-13 May 2004.
  • [18] A. H. Niasar, A. Vahedi and H. Moghbelli, ANFIS-Based Controller with Fuzzy Supervisory Learning for Speed Control of 4-Switch Inverter Brushless DC Motor Drive, 37th IEEE Conference on Power Electronics, pp.1-5, 18-22 June 2006.
  • [19] C. Lin and Y. Lu, A Neural Fuzzy System with Fuzzy Supervised Learning, IEEE Transaction on Systems, Man and Cybernetics, Vol.26, No.5, October 1996.
  • [20] N. Sujatha, K. Vaisakh and K. Anand, Artificial Intelligence Based Speed Control of Brushless DC Motor, IEEE Conference on Power and Energy Society General Meeting, pp.1-6, July 2010.
  • [21] G. Qin, W. Yao and W. Zhang, Design of Nonlinear Optimization PID Controller for BLDCM Based on Neuro- Fuzzy, Proceedings of the 8th International Conference on Electrical Machines and Systems, Vol.2, pp.1524-1527, September 2006.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPOB-0048-0051
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