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- W2073303702 abstract "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. 요 약 본 논문은 비선형 동적 신경망을 이용하여 직접 제어에 관한 연구이다. RBF 신경망을 이용한 제어입력과 근사화 오차 및 외란의 영향을 제거하기 위한 보조제어 입력으로 구성하였다 . 외란이나 근사화 오차에 관계없이 플랜트와 기준모델 사이의 오차가 0이 되도록 하는 알고리즘을 구할 수 있었다. 시뮬레이션 결과는 매우 효과적이며 비선형 시스템의 만족스러운 학습 성능을 증명하였다. Key Words : Nonlinear systems, Robustness Control, Plants of Auto-equipment Systems, Dynamic NeuralProcessor, Robustness Control * Corresponding Author : Hyun-Seob Cho(chohs@chungwoon.ac.kr)Received December 31, 2009 Revised February 26, 2010 Accepted May 13, 2010" @default.
- W2073303702 created "2016-06-24" @default.
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- W2073303702 date "2010-05-31" @default.
- W2073303702 modified "2023-09-23" @default.
- W2073303702 title "Uncertainty-Compensating Neural Network Control for Nonlinear Systems" @default.
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- W2073303702 doi "https://doi.org/10.5762/kais.2010.11.5.1597" @default.
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