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Identification of Fractional-Order Continuous-Time Hybrid Box-Jenkins Models Using Refined Instrumental Variable Continuous-Time Fractional-Order Method

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 240))

Abstract

This paper illustrates the identification of a Box-Jenkins model from sampled input and output data. This is achieved by extending a refined instrumental variable continuous-time (RIVC) method to a refined instrumental variable continuous-time fractional-order (RIVCF) method. The model is a hybrid of continuous and discrete-time as well as fractional and integer-orders. The model consists of a fractional-order linear continuous-time (FLC) transfer function and noise. The FLC transfer function represents the noise free system and the noise represents an integer-order discrete-time autoregressive moving average (ARMA). Monte Carlo simulation analysis is applied for illustrating the performance of the proposed RIVCF method.

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Author information

Authors and Affiliations

  1. Control Theory and Applications Centre, Coventry University, Coventry, CV1 5FB, UK

    Walid Allafi & Keith J. Burnham

Authors
  1. Walid Allafi
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  2. Keith J. Burnham
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Corresponding author

Correspondence to Walid Allafi .

Editor information

Editors and Affiliations

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

    Jerzy Swiątek

  2. Institute of Informatics, Wroclaw University of Technology, Wrocław, Poland

    Adam Grzech

  3. Institute of Informatics, Wroclaw University of Technology, Wrocław, Poland

    Paweł Swiątek

  4. Institute of Informatics, Wroclaw University of Technology, Wrocław, Poland

    Jakub M. Tomczak

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© 2014 Springer International Publishing Switzerland

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Allafi, W., Burnham, K.J. (2014). Identification of Fractional-Order Continuous-Time Hybrid Box-Jenkins Models Using Refined Instrumental Variable Continuous-Time Fractional-Order Method. In: Swiątek, J., Grzech, A., Swiątek, P., Tomczak, J. (eds) Advances in Systems Science. Advances in Intelligent Systems and Computing, vol 240. Springer, Cham. https://doi.org/10.1007/978-3-319-01857-7_75

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  • DOI: https://doi.org/10.1007/978-3-319-01857-7_75

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-01856-0

  • Online ISBN: 978-3-319-01857-7

  • eBook Packages: EngineeringEngineering (R0)

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