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Journal of Zhejiang University SCIENCE A 2004 Vol.5 No.1 P.62~67

http://doi.org/10.1631/jzus.2004.0062


Hybrid internal model control and proportional control of chaotic dynamical systems


Author(s):  QI Dong-lian, YAO Liang-bin

Affiliation(s):  College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   ldq0924@china.com.cn

Key Words:  Chaos, Neural network, Internal model control, Proportional control


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QI Dong-lian, YAO Liang-bin. Hybrid internal model control and proportional control of chaotic dynamical systems[J]. Journal of Zhejiang University Science A, 2004, 5(1): 62~67.

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author="QI Dong-lian, YAO Liang-bin",
journal="Journal of Zhejiang University Science A",
volume="5",
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pages="62~67",
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publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2004.0062"
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2004.0062

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T1 - Hybrid internal model control and proportional control of chaotic dynamical systems
A1 - QI Dong-lian
A1 - YAO Liang-bin
J0 - Journal of Zhejiang University Science A
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.2004.0062


Abstract: 
A new chaos control method is proposed to take advantage of chaos or avoid it. The hybrid internal model control and proportional control learning scheme are introduced. In order to gain the desired robust performance and ensure the system's stability, Adaptive Momentum Algorithms are also developed. Through properly designing the neural network plant model and neural network controller, the chaotic dynamical systems are controlled while the parameters of the BP neural network are modified. Taking the Lorenz chaotic system as example, the results show that chaotic dynamical systems can be stabilized at the desired orbits by this control strategy.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1] Alsing, P.M. and Garielides, A., 1994. Using neural networks for controlling chaos. Phys. Rev. E., 49:1225-1231.

[2] Datta, A. and Choa, J.O., 1996.Adaptive internal model control design and stability analysis. Automatics, 32(2):261-266.

[3] He, B. and Chen, Y., 2002. Using chaos to improve measurement precision. Journal of Zhejiang University SCIENCE, 3(1):47-51.

[4] Kalman, R.E., 1956.Nonlinear aspect of sampled-data control systems. Proc. Nonlinear Circuit Analysis, p.273-313.

[5] Liu, S. and Asada, H.,1996. Teaching and learning of debarring robots using neural networks. Neural Networks Applications, IEEE Technology Update Series.

[6] Lorenz, E.N., 1963. Deterministic non-periodic flows. J. Atmos. Sci., 20:130-141.

[7] Otawara, K. and Fan, L.T., 1995.Controlling chaos with an artificial neural network. Proc. IEEE Int. Joint Conf. Fuzzy Sys., 4:1943-1948.

[8] Ott, E., Grebogi, C.and York, J.A., 1990.Controlling chaos. Phys. Rev. Lett., 64:1196-1199.

[9] Tong, Q.Y., Fan, Y.L. and Li, Y.,2002. Application of chaotic theory to parameter estimation. Journal of Zhejiang University SCIENCE,3(1):42-46.

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