The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Deadzone compensation in motion control systems using neural networks
Abstract
A compensation scheme is presented for general nonlinear actuator deadzones of unknown width. The compensator uses two neural networks (NNs), one to estimate the unknown deadzone and another to provide adaptive compensation in the feedforward path. The compensator NN has a special augmented form containing extra neurons whose activation functions provide a "jump function basis set" for approximating piecewise continuous functions. Rigorous proofs of closed-loop stability for the deadzone compensator are provided and yield tuning algorithms for the weights of the two NNs. The technique provides a general procedure for using NNs to determine the preinverse of an unknown right-invertible function.
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Cites
Approximation by superpositions of a sigmoidal function
Cybenko, G. (1989) · Mathematics of Control, Signals, and Systems · doi:10.1007/bf02551274 · Cited by 13,883
Cited by
Zhang, T. P., & Ge, S. S. (2007) · Automatica · doi:10.1016/j.automatica.2006.12.014 · Cited by 508
Ni, J., Wu, Z., Liu, L., & Liu, C. (2020) · ISA Transactions · doi:10.1016/j.isatra.2019.07.013 · Cited by 105
Ibrir, S., & Su, C.-Y. (2017) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2016.2597745 · Cited by 19
Metadata
- Authors
- Selmic, R. R., & Lewis, F. L.
- Venue
- IEEE Transactions on Automatic Control
- Published
- 2000-04
- Volume
- 45
- Issue
- 4
- Pages
- 602-613
- ISSN
- 0018-9286 (ISSN-L)
- OpenAlex
- W2116490015
- Cited by
- 358
- References
- 1
- FWCI
- 11.83
- Type
- journal article
- Language
- en
- Open access
- closed
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- Selmic, R. R., & Lewis, F. L. (2000). Deadzone compensation in motion control systems using neural networks. IEEE Transactions on Automatic Control. https://doi.org/10.1109/9.847098
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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