Abstract
In this paper, a hybrid neuro-fuzzy controller (NFC) is presented for the speed control of brushless DC motors to improve the control performance of the drive under transient and steady state conditions. In the hybrid control system, proportional-derivative (PD) type neuro-fuzzy controller (NFC) is the main tracking controller, and an integral compensator is proposed to compensate the steady state errors. A simple and smooth activation mechanism described for integral compensator modifies the control law adaptively. The presented BLDC drive has fast tracking capability, less steady state error and robust to load disturbance, and do not need complicated control method. Experimental results showing the effectiveness of the proposed control system are presented.
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References
Rahman, M.A., Zhow, P.: Analysis of brushless permanent magnet synchronous motors. IEEE Transactions on Industrial Electronics 43, 256–267 (1996)
Boldea, I., Nasar, S.A.: Vector Control of AC Drives. CRC Pres, New York (1992)
Gokbulut, M.: Adaptive control of brushless DC motors using neural networks, Ph.D Thesis, Erciyes University Kayseri (1998)
El-Sharkawi, M.A.: Development and implementation of high performance variable structure tracking control for brushless motors. IEEE Trans. on Energy Conversion 6, 114–119 (1991)
El-Samahy, A.A., El-Sharkawi, M.A., Sharaf, S.M.: Adaptive multi-layer self-tuning tracking control for DC brushless motors. IEEE Trans. on Energy Conversion 9, 311–316 (1994)
Da, F., Song, W.: Fuzzy neural networks for direct adaptive control. IEEE Transactions on Industrial Electronics 50, 507–513 (2003)
Lazerini, B., Reyneri, L.M., Chiaberge, M.: A neuro-fuzzy Approach to hybrid intelligent control. IEEE Transactions on Industry Applications 35, 413–425 (1999)
Chen, Y.C., Teng, C.C.: A model reference control structure using a fuzzy neural network. Fuzzy Sets and Systems 73, 291–312 (1995)
Dandil, B.: Robust speed control of induction motors using neuro-fuzzy controllers, Ph.D Thesis. Firat University Elazig (2004)
Rubai, A., Ricketts, D., Kanham, D.: Development and implementation of an adaptive fuzzy-neural network controller for brushless drivers. IEEE Transaction on Industry Applications 38, 441–447 (2002)
Wai, R.J., Lin, F.J.: Fuzzy neural network sliding mode position controller for induction motor drive. IEE Proc. Electrical Power Appl. 146, 297–308 (1999)
Lin, F.J., Wai, R.J., Chen, H.P.: A PM synchronous servo motor drive with an on-line trained fuzzy neural network controller. IEEE Transactions on Energy Conversion 13, 319–325 (1998)
Er, M.J., Gao, Y.: Robust adaptive control of robot manipulators using generalized fuzzy neural networks. IEEE Transactions on Industrial Electronics 50, 620–628 (2003)
Lin, F.J., Wai, R.J.: Adaptive fuzzy-neural network control for induction spindle motor drive. IEEE Trans. on Energy Conversion 17, 507–513 (2002)
Lee, C.H., Teng, C.C.: Identification and control of dynamic systems using recurrent fuzzy neural Networks. IEEE Transactions on Fuzzy System 8, 349–366 (2000)
Lin, F.J., Wai, R.J.: Hybrid control using recurrent fuzzy neural networks for linear induction motor servo drive. IEEE Transactions on Fuzzy system 9, 102–115 (2001)
Lai, Y.S., Lin, J.C.: New hybrid fuzzy controller for direct torque control induction motor drives. IEEE Transactions on Power Electronics 18, 1211–1219 (2003)
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Gökbulut, M., Dandil, B., Bal, C. (2006). A Hybrid Neuro-Fuzzy Controller for Brushless DC Motors. In: Savacı, F.A. (eds) Artificial Intelligence and Neural Networks. TAINN 2005. Lecture Notes in Computer Science(), vol 3949. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11803089_15
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DOI: https://doi.org/10.1007/11803089_15
Publisher Name: Springer, Berlin, Heidelberg
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