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Explicit NMPC Based on Neural Network Models

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Explicit Nonlinear Model Predictive Control

Part of the book series: Lecture Notes in Control and Information Sciences ((LNCIS,volume 429))

Abstract

This chapter considers an approximate mp-NLP approach to explicit solution of deterministic NMPC problems for constrained nonlinear systems described by neural network NARX models. The approach builds an orthogonal search tree structure of the regressor space partition and consists in constructing a piecewise linear (PWL) approximation to the optimal control sequence. A dual-mode control strategy is proposed in order to achieve an offset-free closed-loop response in the presence of bounded disturbances and/or model errors. It consists in using the explicit NMPC (based on NARX model) when the output variable is far from the origin and applying an LQR in a neighborhood of the origin. The LQR design is based on an augmented linear ARX model which takes into account the integral regulation error. The approximate mp-NLP approach and the dual-mode approach are applied to design an explicit output-feedback NMPC for regulation of a pH maintaining system.

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Authors and Affiliations

  1. Institute of System Engineering and Robotics, Bulgarian Academy of Sciences, Acad. G. Bonchev str., Bl. 2, 1113, Sofia, Bulgaria

    Alexandra Grancharova

  2. Department of Engineering Cybernetics, Norwegian University of Science and Technology, O.S. Bragstads plass 2D, 7491, Trondheim, Norway

    Tor Arne Johansen

Authors
  1. Alexandra Grancharova
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  2. Tor Arne Johansen
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Correspondence to Alexandra Grancharova .

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Grancharova, A., Johansen, T.A. (2012). Explicit NMPC Based on Neural Network Models. In: Explicit Nonlinear Model Predictive Control. Lecture Notes in Control and Information Sciences, vol 429. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28780-0_8

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  • DOI: https://doi.org/10.1007/978-3-642-28780-0_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28779-4

  • Online ISBN: 978-3-642-28780-0

  • eBook Packages: EngineeringEngineering (R0)

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