NDLI: Nonlinear Dynamical Decoupling Controller using Multiple Neural Network Models

Content Provider	IEEE Xplore Digital Library
Author	Xin Wang Zhongjie Wang Shaoyuan Li Hui Yang
Copyright Year	2006
Description	Author affiliation: Inst. of Inf. & Control Technol., Shanghai Jiao Tong Univ. (Xin Wang)
Abstract	A multiple models neural network decoupling controller is designed to control the multivariable nonlinear discrete time system. First, at each equilibrium point, one neural network is trained offline to identify the linear term of the nonlinear system while the other neural network is trained online to identify the nonlinear term. Then the multiple models is composed of all models which is got from each equilibrium point. According to the switching index, the best model is selected as the system model. So, for the selected model, the nonlinear term is viewed as the measurable disturbance and eliminated using feedforward strategy. By the choice of the polynomial matrices, the poles of the linear term can be placed arbitrarily and the dynamical decoupling controller is designed accordingly. The simulation example shows effectiveness of the proposed system
Starting Page	2763
Ending Page	2767
File Size	215649
Page Count	5
File Format	PDF
ISBN	1424403324
DOI	10.1109/WCICA.2006.1712867
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Automatic control Nonlinear control systems Electronic mail Design engineering Design automation Discrete time systems Nonlinear systems Polynomials Intelligent control minimum phase system multiple models neural network nonlinear decoupling
Content Type	Text
Resource Type	Article

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