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AUTOMATICA
2006

Enlarging the terminal region of nonlinear model predictive control using the support vector machine method

8 years 4 months ago
Enlarging the terminal region of nonlinear model predictive control using the support vector machine method
In this paper, Receding Horizon Model Predictive Control (RHMPC) of nonlinear systems subject to input and state constraints is considered. We propose to estimate the terminal region and the terminal cost off-line using support vector machine learning. The proposed approach exploits the freedom in the choices of the terminal region and terminal cost needed for asymptotic stability. The resulting terminal regions are large and, hence provide for large domains of attraction of the RHMPC. The promise of the method is demonstrated with two examples. Key words: Nonlinear Model Predictive Control; Support Vector Machine; Constraints; Stability; Terminal conditions.
Chong Jin Ong, Dan Sui, Elmer G. Gilbert
Added 10 Dec 2010
Updated 10 Dec 2010
Type Journal
Year 2006
Where AUTOMATICA
Authors Chong Jin Ong, Dan Sui, Elmer G. Gilbert
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