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Optimising Predictive Control Based on Neural Models

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Artificial Intelligence: Methodology, Systems, and Applications (AIMSA 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5253))

Abstract

This paper presents a Model Predictive Control (MPC) algorithm for on-line economic optimisation of nonlinear technological processes. The economic profit is explicitly expressed in the minimised objective function. The algorithm uses only one dynamic neural model, which is linearised on-line. As a result, an easy to solve on-line quadratic programming problem is formulated. In contrast to the classical multilayer control system structure, the necessity of repeating two nonlinear optimisation problems at each sampling instant is avoided.

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Authors and Affiliations

  1. Institute of Control and Computation Engineering, Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665, Warsaw, Poland

    Maciej Ławryńczuk

Authors
  1. Maciej Ławryńczuk
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Editor information

Danail Dochev Marco Pistore Paolo Traverso

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© 2008 Springer-Verlag Berlin Heidelberg

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Ławryńczuk, M. (2008). Optimising Predictive Control Based on Neural Models. In: Dochev, D., Pistore, M., Traverso, P. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2008. Lecture Notes in Computer Science(), vol 5253. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85776-1_11

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  • DOI: https://doi.org/10.1007/978-3-540-85776-1_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-85775-4

  • Online ISBN: 978-3-540-85776-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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