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Explicit output-feedback nonlinear predictive control based on black-box models
- Source :
-
Engineering Applications of Artificial Intelligence . Mar2011, Vol. 24 Issue 2, p388-397. 10p. - Publication Year :
- 2011
-
Abstract
- Abstract: Nonlinear model predictive control (NMPC) algorithms are based on various nonlinear models. A number of on-line optimization approaches for output-feedback NMPC based on various black-box models can be found in the literature. However, NMPC involving on-line optimization is computationally very demanding. On the other hand, an explicit solution to the NMPC problem would allow efficient on-line computations as well as verifiability of the implementation. This paper applies an approximate multi-parametric nonlinear programming approach to explicitly solve output-feedback NMPC problems for constrained nonlinear systems described by black-box models. In particular, neural network models are used and the optimal regulation problem is considered. A dual-mode control strategy is employed in order to achieve an offset-free closed-loop response in the presence of bounded disturbances and/or model errors. The approach is applied to design an explicit NMPC for regulation of a pH maintaining system. The verification of the NMPC controller performance is based on simulation experiments. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09521976
- Volume :
- 24
- Issue :
- 2
- Database :
- Academic Search Index
- Journal :
- Engineering Applications of Artificial Intelligence
- Publication Type :
- Academic Journal
- Accession number :
- 57370385
- Full Text :
- https://doi.org/10.1016/j.engappai.2010.10.009