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Robust MPC Based on Multivariable RBF-ARX Model for Nonlinear Systems

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4 Author(s)
Hui Peng ; School of Information Science & Engineering, Central South University, Changsha, Hunan, 410083, China (phone: +86-731-8830642; fax: +86-731-8830642; e-mail: huipeng@mail.csu.edu.cn). ; Weihua Gui ; K. Nakano ; H. Shioya

For a class of smooth nonlinear multivariable systems whose working-points vary with time and which may be represented by a linear MIMO ARX model at each working-point, a combination of a local linearization and a polytopic uncertain linear parameter-varying (LPV) state-space model are built to approximate the present and the future system's nonlinear behavior respectively. The combination models are constructed on the basis of a matrix polynomial MIMO RBF-ARX model identified offline for characterizing the underlying nonlinear system. A min-max robust MPC strategy is investigated for the systems based on the approximate models proposed. The closed loop stability of the MPC algorithm is guaranteed by the use of time-varying parameter-dependent Lyapunov function and the feasibility of the linear matrix inequalities (LMIs). The effectiveness of the modeling and control methods proposed in this paper is illustrated by a case study of a thermal power plant simulator.

Published in:

Proceedings of the 44th IEEE Conference on Decision and Control

Date of Conference:

12-15 Dec. 2005