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

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 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.

LMI-Based Analysis and Stabilization of Nonlinear Descriptors with Multiple Delays via Delayed Nonlinear Controller Schemes

“…e numerical complexity of the LMI problems in the above results can be approximated by log 10 � (n 3 d n l ), where n l is the number of total LMI rows and n d is the number of scalar decision variables [70]. Results in eorem 2 can be directly applied for realworld setups; nevertheless, the LMIs might render controller gains whose magnitude cannot be applied in practice.…”