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CLC number: TP183
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Received: 1998-12-12
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A REAL-TIME ADAPTIVE CONTROL ALGORITHM USING NEURAL NETS WITH PERTURBATION
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YANG Jian-gang, WANG Kai, YANG Hua-yong, ZHANG Jian-min. A REAL-TIME ADAPTIVE CONTROL ALGORITHM USING NEURAL NETS WITH PERTURBATION[J]. Journal of Zhejiang University Science A, 2000, 1(1): 61-65.
@article{title="A REAL-TIME ADAPTIVE CONTROL ALGORITHM USING NEURAL NETS WITH PERTURBATION",
author="YANG Jian-gang, WANG Kai, YANG Hua-yong, ZHANG Jian-min",
journal="Journal of Zhejiang University Science A",
volume="1",
number="1",
pages="61-65",
year="2000",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2000.0061"
}
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%T A REAL-TIME ADAPTIVE CONTROL ALGORITHM USING NEURAL NETS WITH PERTURBATION
%A YANG Jian-gang
%A WANG Kai
%A YANG Hua-yong
%A ZHANG Jian-min
%J Journal of Zhejiang University SCIENCE A
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%N 1
%P 61-65
%@ 1869-1951
%D 2000
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2000.0061
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T1 - A REAL-TIME ADAPTIVE CONTROL ALGORITHM USING NEURAL NETS WITH PERTURBATION
A1 - YANG Jian-gang
A1 - WANG Kai
A1 - YANG Hua-yong
A1 - ZHANG Jian-min
J0 - Journal of Zhejiang University Science A
VL - 1
IS - 1
SP - 61
EP - 65
%@ 1869-1951
Y1 - 2000
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.2000.0061
Abstract: This paper proposes an adaptive algorithm of neural nets with a special perturbation for a real time velocity control system of a VVVF(Variable Voltage Variable Frequency) hydraulic elevator. The weight vector of the neural network is adaptively adjusted by the LMS (Least Mean Square) with perturbation, so it is not necessary to know the nonlinear continuous function of the control system. The nonlinear velocity control system is considered as the controller output function in an adaptive controller model. The experimental results obtained from the VVVF hydraulic elevator showed that the neural nets controller using the perturbation algorithm proposed are much stabler and faster in dynamic response compared with the conventional PID (Proportion-Integration-Derivation) controller.
[1]Albus, J. S., 1975. A new approach to manipulator control: the cerebellar model articulation controller (CMAC). Journal of Dynamic System, Measurement and Control, Trans. ASME, 97(3): 220-227.
[2]Miller, W. T., Hewes, R. P., Glanz, F. H., et al., 1990. Real-time dynamic control of an industrial manipulator using a neural-network-based learning controller. IEEE Trans. on Robotics and Automation, 6(1): 1-9.
[3]Yang, Jiangang, 1993, Real time dynamic control of a double inverted pendulum using ameliorated CMAC network. The 1st Congress of Post-Doctoral of China ,National Defense Industry Press, Beijing, p.358-361.
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