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http://dx.doi.org/10.5762/KAIS.2010.11.5.1597

Uncertainty-Compensating Neural Network Control for Nonlinear Systems  

Cho, Hyun-Seob (Dept. of Digital Broadcasting & Electronic Engineering, Chungwoon Univ.)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.11, no.5, 2010 , pp. 1597-1600 More about this Journal
Abstract
In this paper, a direct controller for nonlinear plants using a neural network is presented. The composed of the control input by using RBF neural networks and auxiliary input to compensate for effects of the approximation errors and disturbances. In the results, using this scheme, the output tracking error between the plant and the reference model can asymptotically converge to zero in the presence of bounded disturbances and approximation errors. Simulation results show that it is very effective and can realize a satisfactory control of the nonlinear system.
Keywords
Nonlinear systems; Robustness Control; Plants of Auto-equipment Systems; Dynamic Neural Processor; Robustness Control;
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