Control of magnetic bearing systems via the Chebyshev polynomial-based unified model (CPBUM) neural network

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Volumn 30, Issue 1, 2000, Pages 85-92

  Jeng, Jin Tsong ^a,b   Lee, Tsu Tian ^a,c  

^a IEEE   (Taiwan)

^b HWA HSIA INSTITUTE OF TECHNOLOGY   (Taiwan)

^c NATIONAL TAIWAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

CHEBYSHEV POLYNOMIAL-BASED UNIFIED MODEL (CPBUM) NEURAL NETWORKS;

INTELLIGENT CONTROL; LEARNING ALGORITHMS; LEARNING SYSTEMS; MAGNETIC BEARINGS; MATHEMATICAL MODELS; ONLINE SYSTEMS; POLYNOMIALS;

NEURAL NETWORKS;

EID: 0033893447     PISSN: 10834419     EISSN: None     Source Type: Journal    
DOI: 10.1109/3477.826949     Document Type: Article

References (11)

1. B. Shafai, S. Beale, P. LaRocca, and E. Cusson, "Magnetic bearing control systems and adaptive forced balancing," IEEE Contr. Syst., vol. 14, pp. 4-13, 1994.
2. C. R. Knospe and E. G. Collins, "Special issue on magnetic bearing control," IEEE Trans. Contr. Syst. Technol., vol. 4, pp. 481-598, 1996.
3. K. S. Narendra and K. Parthasarathy, "Identification and control of dynamical systems using neural networks, "IEEE Trans. Neural Networks, vol. 1, pp. 4-27, 1990.
4. L. Jin, P. N. Nikiforuk, and M. M. Gupta, "Fast neural learning and control of discrete-time nonlinear systems," IEEE Trans. Syst., Man, Cybern., vol. 25, pp. 478-488, 1995.
5. G. A. Rovithakis and M. A. Christodoulou, "Direct adaptive regulation of unknown nonlinear dynamical systems via dynamic neural networks," IEEE Trans.Syst., Man, Cybern., vol. 25, pp. 1578-1594, 1995.
6. J. C. Spall and J. A. Cristion, "A neural network controller for systems with unmodeled dynamics with applications to wastewater treatment," IEEE Trans. Syst., Man, Cybern. B, vol. 27, pp. 369-375, 1997.
7. S. Chen and S. A. Billings, "Neural networks for nonlinear dynamic system modeling and identification," Int. J. Contr., vol. 56, no. 2, 1992.
8. T.-T. Lee and J.-T. Jeng, "The Chebyshev polynomial based (CPB) unified model neural networks for function approximation," IEEE Trans. Syst., Man, Cybern. B, vol. 28, pp. 925-935, 1998.
9. W. M. Boothby, An Introduction to Differentiable Manifolds and Riemannian Geometry. New York: Academic, 1996.
10. J. T. Rivlin, Chebyshev Polynomials From Approximation Theory to Algebra and Number Theory. New York: Wiley, 1990.
11. S. Haykin, Adaptive Filter Theory. Englewood Cliffs, NJ: Prentice-Hall, 1997.