The DOI matches its Crossref record.
Stable adaptive neural control scheme for nonlinear systems
Abstract
Based on the Lyapunov synthesis approach, several adaptive neural control schemes have been developed during the last few years. So far, these schemes have been applied only to simple classes of nonlinear systems. This paper develops a design methodology that expands the class of nonlinear systems that adaptive neural control schemes can be applied to and relaxes some of the restrictive assumptions that are usually made. One such assumption is the requirement of a known bound on the network reconstruction error. The overall adaptive scheme is shown to guarantee semiglobal uniform ultimate boundedness. The proposed feedback control law is a smooth function of the state.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
No references could be resolved yet.
Research works printed but not linked: 18
- 10.1109/72.80202 · DOI · Paper page
- polycarpou, 1992, modelling, identification and stable adaptive control of continuous-time nonlinear dynamical systems using neural networks, 1992 American Control Conference ACC, 36, 10.23919/ACC.1992.4792014 · DOI
- sanner, 1991, gaussian networks for direct adaptive control, 1991 American Control Conference ACC, 2153, 10.23919/ACC.1991.4791778 · DOI · Paper page
- 10.1109/72.363477 · DOI · Paper page
- 10.1109/72.485674 · DOI · Paper page
- 10.1002/rnc.4590020105 · DOI
- 10.1109/9.384214 · DOI · Paper page
- poggio, 1990, regularization algorithms for learning that are equivalent to multilayer networks, Sci, 247, 978, 10.1126/science.247.4945.978 · DOI · Paper page
- 10.1115/1.2899076 · DOI
- 10.1007/978-3-662-02581-9 · DOI · Paper page
- 10.1109/5.119551 · DOI
- 10.1109/9.100933 · DOI · Paper page
- krstic, 1995, Nonlinear and Adaptive Control Design
- gupta, 1994, Neuro‐ Control Systems Theory and Applications
- 10.1109/9.376055 · DOI
- ioannou, 1995, Robust Adaptive Control
- narendra, 1989, Stable Adaptive Systems
- white, 1993, Handbook of Intelligent Control Neural Fuzzy and Adaptive Approaches
Cited by
Adaptive Neural Control of Uncertain MIMO Nonlinear Systems
Ge, S. S., & Wang, C. (2004) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.826130 · Cited by 971
Song, X., Sun, P., Song, S., & Stojanovic, V. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08361-y · Cited by 119
A Survey of Deep Learning Applications to Autonomous Vehicle Control
Kuutti, S., Bowden, R., Jin, Y., Barber, P., & Fallah, S. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2962338 · Cited by 96
Jiao, J., Li, J., Chen, J., & Zhang, R. (2026) · Asian Journal of Control · doi:10.1002/asjc.3705 · Cited by 2
Bhan, L., Krstic, M., & Shi, Y. (2025) · arXiv · doi:10.48550/arxiv.2509.26443 · Cited by 4
“This is similar to the results of robust adaptive control [19,20]- although significantly more challenging to the adaptive parameter being the delay on the actuator.”
Metadata
- Authors
- Polycarpou, M. M.
- Venue
- IEEE Transactions on Automatic Control
- Published
- 1996-03
- Volume
- 41
- Issue
- 3
- Pages
- 447-451
- ISSN
- 0018-9286 (ISSN-L)
- OpenAlex
- W2051671655
- Cited by
- 1,547
- References
- 0
- FWCI
- 29.62
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
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.
