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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 H ∞-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 1 Introduction An accurate control of a nonlinear plant is usually based on the knowledge of a mathematical model of the process. Such representation might be obtained by means of first principles Communicated by Anibal Tavares de Azevedo.

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