Neural Computing and Applications

, Volume 23, Supplement 1, pp 323–331 | Cite as

Hierarchical fuzzy CMAC control for nonlinear systems

  • Floriberto Ortiz Rodríguez
  • José de Jesús Rubio
  • Carlos R. Mariaca Gaspar
  • Julio César Tovar
  • Marco A. Moreno Armendáriz
Original Article

Abstract

In this study, a novel indirect adaptive controller is introduced for a class of unknown nonlinear systems. The proposed method provides a simple control architecture that merges from the cerebellar model articulation controller (CMAC) network and hierarchical fuzzy logic; therefore, the complicated CMAC structure can be simplified. The overall adaptive scheme guarantees the uniform stability of the closed-loop system. A simulation is presented to demonstrate the performance of the proposed methodology.

Keywords

Adaptive control Neural networks Fuzzy systems Nonlinear system 

Notes

Acknowledgments

The authors are grateful to the editors and reviewers for their valuable comments and insightful suggestions, which helped to improve this research significantly. The authors thank the Secretaría de Investigación y Posgrado, Comisión de Operación y Fomento de Actividades Académicas del IPN, and Consejo Nacional de Ciencia y Tecnología for their help in this research. The first author thanks the Secretaría de Investigación y Posgrado of the IPN under Research Grand No. 20113187.

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Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • Floriberto Ortiz Rodríguez
    • 2
  • José de Jesús Rubio
    • 1
  • Carlos R. Mariaca Gaspar
    • 2
  • Julio César Tovar
    • 2
  • Marco A. Moreno Armendáriz
    • 3
  1. 1.Sección de Estudios de Posgrado e Investigación, ESIME AzcapotzalcoInstituto Politecnico NacionalMexicoMexico
  2. 2.Escuela Superior de Ingeniería Mecánica y Eléctrica, ZacatencoInstituto Politécnico NacionalMexicoMexico
  3. 3.Laboratorio de Tiempo Real y Automatización, Centro de Investigación en ComputaciónInstituto Politécnico NacionalMexicoMexico

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