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Exact Takagi-Sugeno descriptor models of recurrent high-order neural networks for control applications

  • Carlos Armenta
  • Miguel BernalEmail author
  • Victor Estrada-Manzo
  • Antonio Sala
Article
  • 23 Downloads

Abstract

This work presents an exact Takagi-Sugeno descriptor model of a recurrent high-order neural network arising from identification of a nonlinear plant. The proposed rearrangement allows exploiting the nonlinear characteristics of the neural model for \(\mathcal H_\infty \)-optimal controller design whose conditions are expressed as linear matrix inequalities. Simulation and real-time results are presented that illustrate the advantages of the proposal.

Keywords

Descriptor system Linear matrix inequality Takagi-Sugeno model Recurrent high-order neural network 

Mathematics Subject Classification

93C10 93C95 93C42 93B36 93B30 93D15 93D05 92B20 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare they have no conflict of interest.

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Copyright information

© SBMAC - Sociedade Brasileira de Matemática Aplicada e Computacional 2019

Authors and Affiliations

  1. 1.Department of Electrical and Electronics EngineeringSonora Institute of TechnologyCiudad ObregonMexico
  2. 2.Department of MechatronicsUniversidad Politécnica de PachucaZempoalaMexico
  3. 3.Instituto U. de Automatica e Informatica IndustrialUniversitat Politecnica de ValenciaValenciaSpain

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