Robust Evolving Granular Feedback Linearization

  • Lucas OliveiraEmail author
  • Anderson Bento
  • Valter Leite
  • Fernando Gomide
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1000)


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.


Robust control Evolving systems Feedback linearization 



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.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Lucas Oliveira
    • 1
    • 2
    Email author
  • Anderson Bento
    • 2
  • Valter Leite
    • 2
  • Fernando Gomide
    • 1
  1. 1.Department of Computer Engineering and Automation, School of Electrical and Computer EngineeringUniversity of CampinasCampinasBrazil
  2. 2.Department of Mechatronics EngineeringFederal Center for Technological Education of Minas GeraisDivinópolisBrazil

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