RBF-ARX Model-based Robust MPC for Nonlinear Systems
| Authors: | Peng Hui, Central South University, China
Gui Weihua, Central South University, China
Zou Runmin, Central South University, China
Youssef Rafi, Central South University, China
Yang Zi-Jiang, Kyushu University, Japan
Shioya Hideo, Bailey Japan Co. Ltd., Japan |
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| Topic: | 2.3 Non-Linear Control Systems |
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| Session: | Nonlinear Model Predictive Control |
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| Keywords: | Nonlinear systems, model predictive control, radial basis function networks, ARX model, robustness, stability, linear matrix inequalities. |
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Abstract
An integrated modeling and robust model predictive control (MPC) approach is proposed for a class of nonlinear systems. The system is identified off-line by a RBF-ARX model having linear ARX model structure and state-dependent Gaussian RBF neural network type coefficients. Based on the RBF-ARX model, a combination of a local linearization model and a polytopic uncertain linear parameter-varying model is built to approximate the present and the future system’s behavior respectively. Based on the approximate models, a min-max robust MPC algorithm with input constraint is designed for the system. The closed loop stability of the MPC strategy is guaranteed by the use of parameter-dependent Lyapunov function and the feasibility of the linear matrix inequalities. Simulation study to a NOx decomposition process illustrates the effectiveness of the modeling and robust MPC approach proposed.
