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Fractional-Order Nonlinear System Identification Using MPC Technique

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Computer Information Systems and Industrial Management (CISIM 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12883))

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Abstract

Many real-world processes exhibit fractional-order dynamics and are described by the non-integer order differential equations. In this paper, we quantify the fitting of the Oustaloup approximation method to the fractional-order state-space system to be obtained in the specified narrow frequency range and order. A novel method of the plant model state estimation using the model predictive control (MPC) technique has been verified on the approximated fractional-order water tanks system. To improve the system tracking and reduce the experimental effort, the Kalman filter (KF) has been connected to the MPC structure. The main objective is to design a control system of the linearized fractional-order system with the tuning of its parameters concerning an additive white noise affecting the output of the system. The presented scheme has been verified using numerical examples, and the results of the prediction of the state are discussed.

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Acknowledgment

The present study was supported by a grant WZ/WI-IIT/4/2020 from the Bialystok University of Technology and was funded from the resources for research by the Ministry of Science and Higher Education.

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Authors and Affiliations

  1. Bialystok University of Technology, Bialystok, Poland

    Wiktor Jakowluk

  2. Wageningen University, Wageningen, Holland

    Sjoerd Boersma

Authors
  1. Wiktor Jakowluk
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  2. Sjoerd Boersma
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Corresponding author

Correspondence to Sjoerd Boersma .

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Editors and Affiliations

  1. Bialystok University of Technology, Bialystok, Poland

    Khalid Saeed

  2. VSB - Technical University of Ostrava, Ostrava, Czech Republic

    Jiří Dvorský

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Jakowluk, W., Boersma, S. (2021). Fractional-Order Nonlinear System Identification Using MPC Technique. In: Saeed, K., Dvorský, J. (eds) Computer Information Systems and Industrial Management. CISIM 2021. Lecture Notes in Computer Science(), vol 12883. Springer, Cham. https://doi.org/10.1007/978-3-030-84340-3_31

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  • DOI: https://doi.org/10.1007/978-3-030-84340-3_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-84339-7

  • Online ISBN: 978-3-030-84340-3

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