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International Fuzzy Systems Association World Congress

IFSA/NAFIPS 2019 2019: Fuzzy Techniques: Theory and Applications pp 442–452Cite as

Robust Evolving Granular Feedback Linearization

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1000))

Abstract

This paper develops an adaptive feedback linearization approach to control nonlinear systems under model mismatch conditions. The approach uses the participatory learning modeling algorithm to estimate the nonlinearities from data streams online, and the certainty equivalence principle to compute the control signal. Simulation experiments with the classic surge tank level control benchmark show that evolving robust granular feedback linearization outperforms exact feedback linearization.

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Acknowledgments

The authors acknowledge the Brazilian National Council for Scientific and Technological Development (CNPq) for grant 305906/2014-3, and the Federal Center for Technological Education of Minas Gerais (CEFET-MG) for their support.

Author information

Authors and Affiliations

  1. Department of Computer Engineering and Automation, School of Electrical and Computer Engineering, University of Campinas, Campinas, SP, Brazil

    Lucas Oliveira & Fernando Gomide

  2. Department of Mechatronics Engineering, Federal Center for Technological Education of Minas Gerais, Divinópolis, MG, Brazil

    Lucas Oliveira, Anderson Bento & Valter Leite

Authors
  1. Lucas Oliveira
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  2. Anderson Bento
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  3. Valter Leite
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  4. Fernando Gomide
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Corresponding author

Correspondence to Lucas Oliveira .

Editor information

Editors and Affiliations

  1. Department of Mathematics, University of Louisiana at Lafayette, Lafayette, LA, USA

    Ralph Baker Kearfott

  2. Center for Computing Research (CIC), Instituto Politecnico Nacional, Ciudad de Mexico, Mexico

    Ildar Batyrshin

  3. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada

    Marek Reformat

  4. Department of Computer Science, University of Texas at El Paso, El Paso, TX, USA

    Martine Ceberio

  5. Department of Computer Science, University of Texas at El Paso , El Paso, TX, USA

    Vladik Kreinovich

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Cite this paper

Oliveira, L., Bento, A., Leite, V., Gomide, F. (2019). Robust Evolving Granular Feedback Linearization. In: Kearfott, R., Batyrshin, I., Reformat, M., Ceberio, M., Kreinovich, V. (eds) Fuzzy Techniques: Theory and Applications. IFSA/NAFIPS 2019 2019. Advances in Intelligent Systems and Computing, vol 1000. Springer, Cham. https://doi.org/10.1007/978-3-030-21920-8_40

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