Optimal Adjustment Control of SISO Nonlinear Systems Based on Multi-Dimensional Taylor Network only by Output Feedback

Abstract:

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On the basis of the original multi-dimensional Taylor network, the control input item is added to constitute the nonlinear dynamic model, which is used to optimally control SISO nonlinear system only by output feedback without both meeting the Lipschitz condition and needing the state observer or the system disturbance estimation. Parameters of the multi-dimensional network with the control input item are trained by the conjugate gradient method. Through the simulation, it is demonstrated that the multi-dimensional Taylor network used as the optimal SISO nonlinear system regulator is effective.

Info:

Periodical:

Advanced Materials Research (Volumes 1049-1050)

Edited by:

X.Y. Huang, X.B. Zhu, K.L. Xu and J.H. Wu

Pages:

1389-1391

Citation:

Q. M. Sun and H. S. Yan, "Optimal Adjustment Control of SISO Nonlinear Systems Based on Multi-Dimensional Taylor Network only by Output Feedback", Advanced Materials Research, Vols. 1049-1050, pp. 1389-1391, 2014

Online since:

October 2014

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$38.00

[1] Z. Qu, Robust control of nonlinear uncertain systems, Wiley, New York, (1998).

[2] V.L. Kharitonov, Asymptotic stability of an equilibrium position of a family of systems of linear differential equations, Differentsialnye. 14 (1978) 2086-(2088).

[3] J.C. Doyle, Analysis of feedback systems with structured uncertainties, IEEE, Proc Part D. 129 (1982) 242-251.

[4] D.D. Siljak, Reliable control using multiple control systems, International Journal of Control. 31 (1980) 303-329.

[5] Z. Qu, Y. Jin, Robust control of nonlinear systems in the presence of unknown exogenous dynamics, IEEE Trans. On Automatic Control. 48 (2003) 336-343.

DOI: https://doi.org/10.1109/tac.2002.808495

[6] A. Xu, Q. Zhang, Nonlinear systems fault diagnosis based on adaptive estimation, Automatica. 40 (2004) 1181-1193.

DOI: https://doi.org/10.1016/j.automatica.2004.02.018

[7] J.J. Hopfeld, D.W. Tsnk, Computing with neural circuits: A model, Science. 233 (1986) 625-633.

[8] G.A. Rovithakis, Stable adaptive neuro-control design via Lyapunov function derivative estimation, Automatica. 37 (2001) 1213-1221.

DOI: https://doi.org/10.1016/s0005-1098(01)00094-2

[9] J.S. Wang, G.C.S. Lee, Self-adaptive recurrent neuro-fuzzy control of an autonomous underwater vehicle, IEEE Trans on Robotics and Automation. 19 (2003) 283-295.

DOI: https://doi.org/10.1109/tra.2003.808865

[10] F.L. Lewis, A. Yesildirek, K. Liu, Multilayer neural net robot controller: structure and stability proofs, Control Theory & Applications. 20 (2003) 70-77. (in Chinese).

DOI: https://doi.org/10.1109/cdc.1993.325703

[11] K.S. Narendra, K. Parthasarathy, Identification and control of dynamical systems using neural network, IEEE Trans on Neural Networks. 1 (1990) 4-27.

DOI: https://doi.org/10.1109/72.80202

[12] B. Zhou, H.S. Yan, Financial time series forecasting based on wavelet and multi-dimensional Taylor network dynamics model, Systems Engineering-Theory & Practice. 33 (2013) 2654-2662. (in Chinese).

[13] B. Zhou, H.S. Yan, Nonlinear system identification and prediction based on dynamics cluster multi-dimensional Taylor network model, Control and Decision. 29 (2014) 33-38. (in Chinese).

[14] Y. Lin, H.S. Yan, B. Zhou, Nonlinear time series prediction method based on multi-dimensional Taylor network and its applications, Control and Decision. 29 (2014) 795-801. (in Chinese).

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