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Journal of Zhejiang University SCIENCE A 2006 Vol.7 No.12 P.1984~1988

http://doi.org/10.1631/jzus.2006.A1984


Predictive control of a class of bilinear systems based on global off-line models


Author(s):  ZHANG Ri-dong, WANG Shu-qing

Affiliation(s):  Institute of Advanced Process Control, National Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   zrd-el@163.com

Key Words:  Bilinear systems, Model predictive control (MPC), Adaptive control, Support vector machine (SVM)


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 A, 2006, 7(12): 1984~1988.

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author="ZHANG Ri-dong, WANG Shu-qing",
journal="Journal of Zhejiang University Science A",
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pages="1984~1988",
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publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2006.A1984"
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T1 - Predictive control of a class of bilinear systems based on global off-line models
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DOI - 10.1631/jzus.2006.A1984


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.

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

Reference

[1] Bloemen, H.H.J., van den Boom, T.J.J., Verbruggen, H.B., 2001. An Optimization Algorithm Dedicated to a MPC Problem for Discrete Time Bilinear Models. Proceedings of the American Control Conference, Arlington, VA, p.2371-2381.

[2] Clarke, D.W., Mohtadi, C., Tuffs, P.S., 1987. Generalized predictive control—part I. the basic algorithm. Automatica, 23(2):137-148.

[3] Fontes, A.B., Maitelli, A.L., Cavalcanti, A.L.O., 2004. Bilinear Compensed Generalized Predictive Control: An Adaptive Approach. 5th Asian Control Conference, Melbourne, Australia, p.1781-1785.

[4] He, J.C., Yang, M.Y., Yu, L., Chen, G.D., 1999. Predictive control of a class of generalized bilinear systems. Mechatronic Engineering, 16(5):225-226 (in Chinese).

[5] Jin, Y.Y., Gu, X.Y., 1990. Improved generalized predictive control. Information and Control, (3):8-14 (in Chinese).

[6] Lakhdari, Z., Mokhtari, M., Lecluse, Y., Provost, J., 1995. Adaptive Predictive Control of a Class of Nonlinear Systems—A Case Study. IFAC Proceedings: Adaptive Systems in Control and Signal Processing, Budapest, Hungary, p.209-214.

[7] Liu, G.Z., Li, P., 2004. Generalized Predictive Control for a Class of Bilinear Systems. IFAC 7th Symposium on Advanced Control of Chemical Processes, Hong Kong, China, p.952-956.

[8] Peng, H., Ozaki, T., Toyoda, Y., Haggan-Ozaki, V., 2002. Nonlinear Predictive Control Based on a Global Model Identified Off-line. Proceedings of the American Control Conference, Anchorage, AK, p.8-10.

[9] Priestley, M.B., 1980. State dependent models: a general approach to nonlinear time series analysis. Journal of Time Series Analysis, 1:57-71.

[10] Suykens, J.A.K., Vandewalle, J., 1999. Least square support vector machine classifiers. Neural Processing Letters, 9(3):293-300.

[11] Yao, X.Y., Qian, J.X., 1997. Generalized predictive control of algorithm of bilinear system. Journal of Zhejiang University: Engineering Science, 31(2):231-236 (in Chinese).

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