| Nonlinear decoupling controller design based on least squares support vector regression |
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| 作者姓名： | 文香军 张雨浓 阎威武 许晓鸣 |
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| 作者单位： | Department of Automatic Control Shanghai Jiao Tong University Shanghai 200030 China，Department of Electronic and Electrical Engineering University of Strathclyde Glasgow G1 1QE UK，Department of Automatic Control Shanghai Jiao Tong University Shanghai 200030 China，Department of Automatic Control Shanghai Jiao Tong University Shanghai 200030 China |
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| 基金项目： | Project supported by the National Basic Research Program (973) of China (No. 2002CB312200), and the Hi-Tech Research and Devel-opment Program (863) of China (No. 2002AA412010) |
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| 摘 要： | INTRODUCTION Most practical systems are multivariate nonlin- ear systems. In general, the MIMO (multiple inputs and multiple outputs) systems are coupled. This cou- pling affects the effectiveness of a specific loop con- troller on the corresponding output, and in some case, may become serious and cause many difficulties to the control system design. How to decouple the mul- tivariate systems and design practical controllers is one of the major issues in nonlinear control area. In recen…
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| 关 键 词： | 非线性解耦控制器 设计 支持向量机 广义逆系统 最小二乘法 |
| 收稿时间： | 2005-04-06 |
| 修稿时间： | 2005-09-01 |
Nonlinear decoupling controller design based on least squares support vector regression |
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| Xiang-jun Wen,Yu-nong Zhang,Wei-wu Yan,Xiao-ming Xu.Nonlinear decoupling controller design based on least squares support vector regression[J].Journal of Zhejiang University Science,2006,7(2):275-284. |
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| Authors: | Xiang-jun Wen Yu-nong Zhang Wei-wu Yan Xiao-ming Xu |
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| Institution: | (1) Department of Automatic Control, Shanghai Jiao Tong University, Shanghai, 200030, China;(2) Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, G1 1QE, UK |
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| Abstract: | 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 un- known or state variables are difficult or impossible to measure. Simulation results showed the efficacy of the method. |
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| Keywords: | Support Vector Machine (SVM) Decoupling control Nonlinear system Generalized inverse system |
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