Neural Computing and Applications

, Volume 23, Supplement 1, pp 323–331

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

DOI: 10.1007/s00521-013-1423-x

Cite this article as:
Rodríguez, F.O., de Jesús Rubio, J., Gaspar, C.R.M. et al. Neural Comput & Applic (2013) 23(Suppl 1): 323. doi:10.1007/s00521-013-1423-x

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 

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