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An optimal tracking neuro-controller for nonlinear dynamic systems
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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Cites
Forward models: supervised learning with a distal teacher
Jordan, M. I., & Rumelhart, D. E. (1992) · Cognitive Science · doi:10.1207/s15516709cog1603_1 · Cited by 1,451
Cited by
Zhang, H., Wei, Q., & Luo, Y. (2008) · IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) · doi:10.1109/tsmcb.2008.920269 · Cited by 489
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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- 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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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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