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Methods of Prediction Improvement in Efficient MPC Algorithms Based on Fuzzy Hammerstein Models

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Transactions on Computational Collective Intelligence XIV

Part of the book series: Lecture Notes in Computer Science ((TCCI,volume 8615))

Abstract

Two methods of prediction improvement in Model Predictive Control (MPC) algorithms utilizing fuzzy Hammerstein models are proposed in the paper. The first one consists in iterative adjustment of the prediction, the second one – in utilization of disturbance measurement. Though the methods can significantly improve control system operation, they modify the prediction in such a way that it is described by relatively simple analytical formulas. Thus, the prediction has such a form that the MPC algorithms using it are formulated as numerically efficient quadratic optimization problems. Efficiency of the MPC algorithms based on the prediction utilizing the proposed methods of improvement is demonstrated in the example control system of a nonlinear control plant with significant time delay.

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Authors and Affiliations

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

    Piotr M. Marusak

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  1. Piotr M. Marusak
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Correspondence to Piotr M. Marusak .

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  1. Institute of Informatics, Wroclaw University of Technology, Wroclaw, Poland

    Ngoc Thanh Nguyen

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Marusak, P.M. (2014). Methods of Prediction Improvement in Efficient MPC Algorithms Based on Fuzzy Hammerstein Models. In: Nguyen, N. (eds) Transactions on Computational Collective Intelligence XIV. Lecture Notes in Computer Science(), vol 8615. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44509-9_8

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  • DOI: https://doi.org/10.1007/978-3-662-44509-9_8

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-44508-2

  • Online ISBN: 978-3-662-44509-9

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