Explicit output-feedback nonlinear predictive control based on black-box models

Nonlinear Model Predictive Control (NMPC) algorithms are based on various nonlinear models. A number of on-line optimization approaches for outputfeedback 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 This work was financed by the National Science Fund of the Ministry of Education, Youth and Science of Republic of Bulgaria, contract DO02-94/14.12.2008 and the Slovenian Research Agency, contract BI-BG/09-10-005 (“Application of Gaussian processes to the modeling and control of complex stochastic systems”). ∗Corresponding author A. Grancharova. Tel. +359 889 625010. Fax +3592 870 33 61. Email addresses: [email protected] (A. Grancharova), [email protected] (J. Kocijan), [email protected] (T. A. Johansen) Preprint submitted to Engineering Applications of Artificial IntelligenceJanuary 19, 2010 NMPC controller performance is based on simulation experiments.