| Predictive control of a class of bilinear systems based on global off-line models |
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| 作者姓名： | ZHANG Ri-dong WANG Shu-qing |
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| 作者单位： | Institute of Advanced Process Control，National Key Laboratory of Industrial Control Technology，Zhejiang University，Hangzhou 310027，China |
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| 基金项目： | Project (No. 60421002) supported by the National Natural ScienceFoundation of China |
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| 摘 要： | INTRODUCTION Bilinear systems are a kind of important nonlinear systems with relatively simple structure, and many industrial processes can be described as a bilinear system. Thus research on the control of this kind of systems is very important. On the other hand, model predictive control (MPC) (Clarke et al., 1987) has been widely used in industrial applications and many predictive control methods focusing on bilinear systems are emerging (Bloemen et al., 2001; Fontes et al., 2004; He…
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| 关 键 词： | 双线性系统 模式预控制 MPC 适应控制 |
| 收稿时间： | 2006-02-10 |
| 修稿时间： | 2006-09-14 |
Predictive control of a class of bilinear systems based on global off-line models |
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| ZHANG Ri-dong WANG Shu-qing.Predictive control of a class of bilinear systems based on global off-line models[J].Journal of Zhejiang University Science,2006,7(12):1984-1988. |
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| Authors: | Ri-dong Zhang Shu-qing Wang |
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| Institution: | (1) Institute of Advanced Process Control, National Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, 310027, China |
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| Abstract: | A new multi-step adaptive predictive control algorithm for a class of bilinear systems is presented. The structure of the
bilinear system is converted into a simple linear model by using nonlinear support vector machine (SVM) dynamic approximation
with analytical control law derived. The method does not need on-line parameters estimation because the system’s internal
model has been transformed into an off-line global model. Compared with other traditional methods, this control law reduces
on-line parameter estimating burden. In addition, its overall linear behavior treating method allows an analytical control
law available and avoids on-line nonlinear optimization. Simulation results are presented in the article to illustrate the
efficiency of the method.
Project (No. 60421002) supported by the National Natural Science Foundation of China |
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| Keywords: | Bilinear systems Model predictive control (MPC) Adaptive control Support vector machine (SVM) |
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