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

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


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.


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

Mathematics Subject Classification

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


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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