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Output feedback control of nonlinear systems using RBF neural networks
Abstract
An adaptive output feedback control scheme for the output tracking of a class of continuous-time nonlinear plants is presented. An RBF neural network is used to adaptively compensate for the plant nonlinearities. The network weights are adapted using a Lyapunov-based design. The method uses parameter projection, control saturation, and a high-gain observer to achieve semi-global uniform ultimate boundedness. The effectiveness of the proposed method is demonstrated through simulations. The simulations also show that by using adaptive control in conjunction with robust control, it is possible to tolerate larger approximation errors resulting from the use of lower order networks.
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Cited by
Adaptive Command-Filtered Backstepping Control of Robot Arms With Compliant Actuators
Pan, Y., Wang, H., Li, X., & Yu, H. (2018) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2017.2695600 · Cited by 210
Metadata
- Authors
- Seshagiri, S., & Khalil, H. K.
- Venue
- IEEE Transactions on Neural Networks
- Published
- 2000
- Volume
- 11
- Issue
- 1
- Pages
- 69-79
- ISSN
- 1045-9227 (ISSN-L)
- OpenAlex
- W2095910574
- Cited by
- 558
- References
- 0
- FWCI
- 33.80
- Type
- journal article
- Language
- en
- Open access
- closed
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- Seshagiri, S., & Khalil, H. K. (2000). Output feedback control of nonlinear systems using RBF neural networks. IEEE Transactions on Neural Networks. https://doi.org/10.1109/72.822511
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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).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
