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Cited by 160

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An optimal tracking neuro-controller for nonlinear dynamic systems

Young-Moon Park, Myeon-Song Choi, Lee, K.Y.·1996·IEEE Transactions on Neural Networks

Abstract

Multilayer neural networks are used to design an optimal tracking neuro-controller (OTNC) for discrete-time nonlinear dynamic systems with quadratic cost function. The OTNC is made of two controllers: feedforward neuro-controller (FFNC) and feedback neuro-controller (FBNC). The FFNC controls the steady-state output of the plant, while the FBNC controls the transient-state output of the plant. The FFNC is designed using a novel inverse mapping concept by using a neuro-identifier. A generalized backpropagation-through-time (GBTT) algorithm is developed to minimize the general quadratic cost function for the FBNC training. The proposed methodology is useful as an off-line control method where the plant is first identified and then a controller is designed for it. A case study for a typical plant with nonlinear dynamics shows good performance of the proposed OTNC.

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Metadata

Authors
Park, Y.-M., Choi, M.-S., & Lee, K. Y.
Venue
IEEE Transactions on Neural Networks
Published
1996
Volume
7
Issue
5
Pages
1099-1110
ISSN
1045-9227 (ISSN-L)
OpenAlex
W2125944702
Cited by
160
References
1
FWCI
6.25
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: An optimal tracking neuro-controller for nonlinear dynamic systems. https://app.cochat.ai/papers/10.1109/72.536307
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Park, Y.-M., Choi, M.-S., & Lee, K. Y. (1996). An optimal tracking neuro-controller for nonlinear dynamic systems. IEEE Transactions on Neural Networks. https://doi.org/10.1109/72.536307

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