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

  • Authors:

  • Alexandra Grancharova;Juš Kocijan;Tor A. Johansen

  • Affiliations:

  • Institute of System Engineering and Robotics, Bulgarian Academy of Sciences, Acad. G. Bonchev str., Bl.2, P.O. Box 79, Sofia 1113, Bulgaria;Department of Systems and Control, Jozef Stefan Institute, Jamova 39, 1000 Ljubljana, Slovenia and Centre for Systems and Information Technologies, University of Nova Gorica, Vipavska 13, 5000 Nov ...;Department of Engineering Cybernetics, Norwegian University of Science and Technology, 7491 Trondheim, Norway

  • Venue:
  • Engineering Applications of Artificial Intelligence
  • Year:
  • 2011

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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.