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Journal of Zhejiang University SCIENCE A 2006 Vol.7 No.2 P.275~284


Nonlinear decoupling controller design based on least squares support vector regression

Author(s):  Wen Xiang-jun, Zhang Yu-nong, Yan Wei-wu, Xu Xiao-ming

Affiliation(s):  Department of Automatic Control, Shanghai Jiao Tong University, Shanghai 200030, China; more

Corresponding email(s):   wenxiangjun@sjtu.edu.cn, ynzhang@ieee.org, yanwwsjtu@sjtu.edu.cn, xmxu@sjtu.edu.cn

Key Words:  Support Vector Machine (SVM), Decoupling control, Nonlinear system, Generalized inverse system

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Wen Xiang-jun, Zhang Yu-nong, Yan Wei-wu, Xu Xiao-ming. Nonlinear decoupling controller design based on least squares support vector regression[J]. Journal of Zhejiang University Science A, 2006, 7(2): 275~284.

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journal="Journal of Zhejiang University Science A",
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%T Nonlinear decoupling controller design based on least squares support vector regression
%A Wen Xiang-jun
%A Zhang Yu-nong
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%A Xu Xiao-ming
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2006.A0275

T1 - Nonlinear decoupling controller design based on least squares support vector regression
A1 - Wen Xiang-jun
A1 - Zhang Yu-nong
A1 - Yan Wei-wu
A1 - Xu Xiao-ming
J0 - Journal of Zhejiang University Science A
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EP - 284
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.2006.A0275

Support Vector Machines (SVMs) have been widely used in pattern recognition and have also drawn considerable interest in control areas. Based on a method of least squares SVM (LS-SVM) for multivariate function estimation, a generalized inverse system is developed for the linearization and decoupling control of a general nonlinear continuous system. The approach of inverse modelling via LS-SVM and parameters optimization using the Bayesian evidence framework is discussed in detail. In this paper, complex high-order nonlinear system is decoupled into a number of pseudo-linear Single Input Single Output (SISO) subsystems with linear dynamic components. The poles of pseudo-linear subsystems can be configured to desired positions. The proposed method provides an effective alternative to the controller design of plants whose accurate mathematical model is unknown or state variables are difficult or impossible to measure. Simulation results showed the efficacy of the method.

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