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RCMAC-based adaptive control design for brushless DC motors

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Abstract

This paper proposes a recurrent cerebellar model articulation controller (RCMAC)-based adaptive control for brushless DC motors. This control system is composed of a RCMAC and a compensation controller. RCMAC is used to mimic an ideal controller, and the compensation controller is designed to compensate for the approximation error between the ideal controller and RCMAC. The Lyapunov stability theory is utilized to derive the parameter tuning algorithm, so that the uniformly ultimately bound stability of the closed-loop system can be achieved. For comparison, a fuzzy control, an adaptive fuzzy control and the developed RCMAC-based adaptive control are implemented on a field programmable gate array chip for controlling a brushless DC motor. Experimental results reveal that the proposed RCMAC-based adaptive control system can achieve the best tracking performance. Moreover, since the developed RCMAC-based adaptive control scheme uses a hyperbolic tangent function to compensate for the approximation error, there is no chattering phenomenon in the control effort. Thus, the proposed control method is more suitable for real-time practical control applications.

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References

  1. Dote Y, Kinoshita S (1990) Brushless servomotors: fundamentals and applications. Clarendon Press, Oxford

    Google Scholar 

  2. Liu Y, Zhu ZQ, Howe D (2005) Direct torque control of brushless DC drives with reduced torque ripple. IEEE Trans Ind Appl 41(2):599–608. doi:10.1109/TIA.2005.844853

    Article  Google Scholar 

  3. Rubaai A, Ricketts D, Kankam MD (2002) Development and implementation of an adaptive fuzzy-neural-network controller for brushless drives. IEEE Trans Ind Appl 38(2):441–447. doi:10.1109/28.993165

    Article  Google Scholar 

  4. Rubaai A, Ofoli AR, Cobbinah D (2007) DSP-based real-time implementation of a hybrid H ∞ adaptive fuzzy tracking controller for servo-motor drives. IEEE Trans Ind Appl 43(2):476–484. doi:10.1109/TIA.2006.889904

    Article  Google Scholar 

  5. Hsu CF, Lin CM, Chen TY (2005) Neural-network-identification-based adaptive control of wing rock motion. IEE Proc Contr Theor Appl 152(1):65–71. doi:10.1049/ip-cta:20050904

    Article  Google Scholar 

  6. Duarte-Mermoud MA, Suarez AM, Bassi DF (2005) Multivariable predictive control of a pressurized tank using neural networks. Neural Comput Appl 15(1):18–25. doi:10.1007/s00521-005-0003-0

    Google Scholar 

  7. Leu YG, Wang WY, Lee TT (2005) Observer-based direct adaptive fuzzy-neural control for nonaffine nonlinear systems. IEEE Trans Neural Netw 16(4):853–861. doi:10.1109/TNN.2005.849824

    Article  Google Scholar 

  8. Lin CM, Hsu CF (2003) Neural network hybrid control for antilock braking systems. IEEE Trans Neural Netw 14(2):351–359. doi:10.1109/TNN.2002.806950

    Article  Google Scholar 

  9. Jan JC, Hung SL (2001) High-order MS CMAC neural network. IEEE Trans Neural Netw 12(3):598–603. doi:10.1109/72.925562

    Article  Google Scholar 

  10. Shiraishi H, Ipri SL, Cho DD (1995) CMAC neural network controller for fuel-injection systems. IEEE Trans Contr Syst Technol 3(2):32–38. doi:10.1109/87.370707

    Article  Google Scholar 

  11. Su SF, Tao T, Hung TH (2003) Credit assigned CMAC and its application to online learning robust controllers. IEEE Trans Syst Man Cybern B 33(2):202–213. doi:10.1109/TSMCB.2003.810447

    Article  Google Scholar 

  12. Lin CM, Peng YF (2004) Adaptive CMAC-based supervisory control for uncertain nonlinear systems. IEEE Trans Syst Man Cybern B 34(2):1248–1260. doi:10.1109/TSMCB.2003.822281

    Article  Google Scholar 

  13. Lin CM, Peng YF (2005) Missile guidance law design using adaptive cerebellar model articulation controller. IEEE Trans Neural Netw 16(3):636–644. doi:10.1109/TNN.2004.839358

    Article  Google Scholar 

  14. Su SF, Lee ZJ, Wang YP (2006) Robust and fast learning for fuzzy cerebellar model articulation controllers. IEEE Trans Syst Man Cybern B 36(1):203–208. doi:10.1109/TSMCB.2005.855570

    Article  Google Scholar 

  15. Lin CM, Chen LY, Chen CH (2007) RCMAC hybrid control for MIMO uncertain nonlinear systems using sliding-mode technology. IEEE Trans Neural Netw 18(30):708–720. doi:10.1109/TNN.2007.891198

    Article  Google Scholar 

  16. Lin CT, Lee CSG (1996) Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems. Prentice-Hall, Englewood Cliffs

    Google Scholar 

  17. Lin CM, Hsu CF (2004) Supervisory recurrent fuzzy neural network control of wing rock for slender delta wings. IEEE Trans Fuzzy Syst 12(5):733–742. doi:10.1109/TFUZZ.2004.834803

    Article  Google Scholar 

  18. Leung CS, Tsoi AC (2005) Combined learning and pruning for recurrent radial basis function networks based on recursive least square algorithms. Neural Comput Appl 15(1):62–78. doi:10.1007/s00521-005-0009-7

    Google Scholar 

  19. Wang LX (1994) Adaptive fuzzy systems and control: design and stability analysis. Prentice-Hall, Englewood Cliffs

    Google Scholar 

  20. Park JH, Seo SJ, Park GT (2003) Robust adaptive fuzzy controller for nonlinear system using estimation of bounds for approximation errors. Fuzzy Sets Syst (133):19–36. doi:10.1016/S0165-0114(02)00137-9

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Acknowledgments

The authors appreciate partial support from the National Science Council of Republic of China under grant NSC 95-2622-E-155-004-CC3.

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Authors and Affiliations

  1. Department of Electrical Engineering, Yuan Ze University, Chung-Li, Tao-Yuan, 320, Taiwan, ROC

    Chih-Min Lin & Chao-Ming Chung

  2. Department of Electrical Engineering, Chung Hua University, Hsinchu, 300, Taiwan, ROC

    Chun-Fei Hsu

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  1. Chih-Min Lin
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  2. Chun-Fei Hsu
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  3. Chao-Ming Chung
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Correspondence to Chih-Min Lin.

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Lin, CM., Hsu, CF. & Chung, CM. RCMAC-based adaptive control design for brushless DC motors. Neural Comput & Applic 18, 781–790 (2009). https://doi.org/10.1007/s00521-008-0230-2

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  • DOI: https://doi.org/10.1007/s00521-008-0230-2

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