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Indirect adaptive neural controller of nonlinear systems using auto-tuning neuron

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper, a novel indirect adaptive neural controller using only two auto-tuning neurons is developed for a class of nonlinear systems. Unlike traditional multi-layered neural controllers, the structure of the proposed controller is very simple and practicable. There are three adjustable parameters in each auto-tuning neuron. Two such auto-tuning neurons used in our proposed indirect adaptive controller are used to track on-line the desired signal. The adaptation law for adjusting these parameters is developed based on the Lyapunov approach. Moreover, the stability of the overall closed-loop system can be analyzed and guaranteed by introducing the additional supervisory controller and the technique of modified adaptation law with projection. Finally, the tracking control of the inverted pendulum system is presented to illustrate the proposed method.
Rocznik
Strony
313--327
Opis fizyczny
Bibliogr. 17 poz.
Twórcy
autor
  • Department of Electrical Engineering National Sun Yat-Sen University Kaohsiung, Taiwan 804, R.O.C.
autor
  • Department of Electrical Engineering National Sun Yat-Sen University Kaohsiung, Taiwan 804, R.O.C.
autor
  • Department of Electrical Engineering National Sun Yat-Sen University Kaohsiung, Taiwan 804, R.O.C.
Bibliografia
  • CHANG, W.O., HWANG, R.C. and HSIEH, J.G. (2002) A self-tuning PID control for a class of nonlinear systems based on the Lyapunov approach. Journal of Process Control, 12, 233-242.
  • CHANG, W.O., HWANG, R.C. and HSIEH, J.G. (1998) Adaptive control ofmultivariable dynamic systems using independent self-tuning neurons. Proceedings of tenth international conference on tools with artificial intelligence, Taipei, Taiwan, 68- 73.
  • CHEN, C.T. and CHANG, W.O. (1996) A feedforward neural network with function shape autotuning. Neural Networks, 9 (4), 627- 641.
  • CUI, X. and SHIN, K.G. (1993) Direct control and coordination using neural networks. IEEE Transac tions on Systems, Man, and Cybernetics, 23(3), 686-697.
  • OucH, W. and JANKOWSKI, N. (1999) Survey of neural transfer functions. Neural Computing Surveys, 2, 163- 213.
  • HORNG, J. H. (1999) Neural adaptive tracking control of a DC motor. Information Sciences, 118, 1-13.
  • JANG, J.S.R. (1992) Self-tuning fuzzy controllers based on temporal back propagation. IEEE Transactions on Neural Networks, 3 (5), 714-723.
  • KHANMOHAMMADI, S., HASSANZADEH , I. and SHARIFIAN, M.B.B. (2000) Modified adaptive discrete control system containing neural estimator and neural controller. Artificial Intelligence in Engineering, 14 (1), 31-38.
  • MAYOSKY, M.A. and CANCELO, G.I.E. (1999) Direct adaptive control of wind energy conversion systems using Gaussian networks. IEEE Transactions on Neural Networks, 10 (4), 898-905.
  • PARK, Y.M., CHOI, M.S. and LEE, K.Y. (1996) An optimal tracking neuroncontroller for nonlinear dynamics systems. IEEE Transactions on Neural Networks, 7 (5), 1099-1110.
  • SASTRY, S. and BODSON, M. (1989) Adaptive Control: Stability, Convergence, and Robustness. Prentice-Hall International, Inc.
  • SLOTINE, J.J.E. and LI, W.P. (1991) Applied Nonlinear Control. Prentice-Hall International.
  • WANG, L.X. (1994) Adaptive Fuzzy Systems and Control: Design and Stability Analysis. PTR Prentice Hall.
  • WANG, L.X. (1996) Stable adaptive fuzzy controllers with application to inverted pendulum tracking. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 26 (5), 677-691.
  • WANG, L.X. (1997) A Course in Fuzzy Systems and Control. Prentice-Hall International.
  • Wu, M.C., LEE, L.C. and SHIH, M.C. (1998) Neuro-fuzzy controller design of the antilock braking system. JSME International Journal, 41 (4), 836-843.
  • ZHIHONG, M., Wu, H.R. and PALANISWAMI, M. (1998) An adaptive tracking controller using neural networks. IEEE Transactions on Neural Networks, 9(5), 947-955.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BAT5-0007-0009
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