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Fuzzy neural network adaptive robust control of a class of nonlinear interconnected system based on T-S fuzzy model

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1 Author(s)
Yan-xin Zhang ; Sch. of Electron. & Inf. Eng., Beijing Jiaotong Univ., Beijing, China

In this paper, a fuzzy neural network control scheme is developed for a class of nonlinear interconnected system. The robust adaptive controller is designed based on the T-S fuzzy model of the original system, which includes three parts: constant state feedback gains matrix for T-S fuzzy model, neural network to approximate the interconnections and an approximation error compensator, which attenuates the effect of neural network approximation error. In this new control scheme, it is not needed to find a common positive definite matrix satisfying Riccati matrix inequality, and neither constraint nor matching conditions is required. The controller not only ensures the stability of closed-loop system, but also realizes the tracking performance objective. The emulation illustrates the validation of the designed scheme.

Published in:

Control and Decision Conference (CCDC), 2010 Chinese

Date of Conference:

26-28 May 2010