SAC for Nonlinear Systems Using Elman Recurrent Neural Networks
Summary :
This paper presents a method of simple adaptive control (SAC) for nonlinear systems using Elman recurrent neural networks (ERNNs). The control input is given by the sum of the output of a simple adaptive controller and the output of the ERNN. The ERNN is used to compensate the nonlinearity of plant dynamics that is not taken into consideration in the usual SAC. The role of the ERNN is to construct a linearized model by minimizing the output error caused by nonlinearities in the control systems.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E85-A No.8 pp.1831-1840
- Publication Date
- 2002/08/01
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Special Section PAPER (Special Section on Digital Signal Processing)
- Category
- Nonlinear Signal Processing
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Jianming LU, Jiunshian PHUAH, Takashi YAHAGI, "SAC for Nonlinear Systems Using Elman Recurrent Neural Networks" in IEICE TRANSACTIONS on Fundamentals,
vol. E85-A, no. 8, pp. 1831-1840, August 2002, doi: .
Abstract: This paper presents a method of simple adaptive control (SAC) for nonlinear systems using Elman recurrent neural networks (ERNNs). The control input is given by the sum of the output of a simple adaptive controller and the output of the ERNN. The ERNN is used to compensate the nonlinearity of plant dynamics that is not taken into consideration in the usual SAC. The role of the ERNN is to construct a linearized model by minimizing the output error caused by nonlinearities in the control systems.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e85-a_8_1831/_p
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@ARTICLE{e85-a_8_1831,
author={Jianming LU, Jiunshian PHUAH, Takashi YAHAGI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={SAC for Nonlinear Systems Using Elman Recurrent Neural Networks},
year={2002},
volume={E85-A},
number={8},
pages={1831-1840},
abstract={This paper presents a method of simple adaptive control (SAC) for nonlinear systems using Elman recurrent neural networks (ERNNs). The control input is given by the sum of the output of a simple adaptive controller and the output of the ERNN. The ERNN is used to compensate the nonlinearity of plant dynamics that is not taken into consideration in the usual SAC. The role of the ERNN is to construct a linearized model by minimizing the output error caused by nonlinearities in the control systems.},
keywords={},
doi={},
ISSN={},
month={August},}
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TY - JOUR
TI - SAC for Nonlinear Systems Using Elman Recurrent Neural Networks
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1831
EP - 1840
AU - Jianming LU
AU - Jiunshian PHUAH
AU - Takashi YAHAGI
PY - 2002
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E85-A
IS - 8
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - August 2002
AB - This paper presents a method of simple adaptive control (SAC) for nonlinear systems using Elman recurrent neural networks (ERNNs). The control input is given by the sum of the output of a simple adaptive controller and the output of the ERNN. The ERNN is used to compensate the nonlinearity of plant dynamics that is not taken into consideration in the usual SAC. The role of the ERNN is to construct a linearized model by minimizing the output error caused by nonlinearities in the control systems.
ER -
English
