The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
Robust Adaptive Dynamic Programming and Feedback Stabilization of Nonlinear Systems
Abstract
This paper studies the robust optimal control design for a class of uncertain nonlinear systems from a perspective of robust adaptive dynamic programming (RADP). The objective is to fill up a gap in the past literature of adaptive dynamic programming (ADP) where dynamic uncertainties or unmodeled dynamics are not addressed. A key strategy is to integrate tools from modern nonlinear control theory, such as the robust redesign and the backstepping techniques as well as the nonlinear small-gain theorem, with the theory of ADP. The proposed RADP methodology can be viewed as an extension of ADP to uncertain nonlinear systems. Practical learning algorithms are developed in this paper, and have been applied to the controller design problems for a jet engine and a one-machine power system.
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
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225
Power System Stability and Control
Kundur, P. (1994) · Cited by 19,639
Nonlinear and adaptive control design
Krstić, M., Kokotović, P. V., & Kanellakopoulos, I. (1995) · Cited by 10,421
Watkins, C. (1989) · OpenGrey (Institut de l'Information Scientifique et Technique) · doi:10.17863/cam.132164 · Cited by 5,297
Learning to predict by the methods of temporal differences
Sutton, R. S. (1988) · Machine Learning · doi:10.1007/bf00115009 · Cited by 5,148
Dynamic Programming and Markov Processes.
Freimer, M., & Howard, R. A. (1961) · Journal of the American Statistical Association · doi:10.2307/2282268 · Cited by 3,378
Neuronlike adaptive elements that can solve difficult learning control problems
Barto, A. G., Sutton, R. S., & Anderson, C. W. (1983) · IEEE Transactions on Systems, Man, and Cybernetics · doi:10.1109/tsmc.1983.6313077 · Cited by 3,233
Beyond Regression : "New Tools for Prediction and Analysis in the Behavioral Sciences
Werbos, P. J. (1974) · Medical Entomology and Zoology · Cited by 2,789
Smooth stabilization implies coprime factorization
Sontag, E. D. (1989) · IEEE Transactions on Automatic Control · doi:10.1109/9.28018 · Cited by 2,717
Lewis, F. L., Vrabie, D. L., & Syrmos, V. L. (2012) · doi:10.1002/9781118122631 · Cited by 2,020
Cited by
Value iteration and adaptive dynamic programming for data-driven adaptive optimal control design
Bian, T., & Jiang, Z.-P. (2016) · Automatica · doi:10.1016/j.automatica.2016.05.003 · Cited by 348
Self-citationGao, W., Jiang, Y., Jiang, Z.-P., & Chai, T. (2016) · Automatica · doi:10.1016/j.automatica.2016.05.008 · Cited by 234
Self-citationGao, W., Jiang, Y., Jiang, Z.-P., & Chai, T. (2014) · Proceeding of the 11th World Congress on Intelligent Control and Automation · doi:10.1109/wcica.2014.7053043 · Cited by 35
Self-citation
Metadata
- Authors
- Jiang, Y., & Jiang, Z.-P.
- Venue
- IEEE Transactions on Neural Networks and Learning Systems
- Published
- 2014-05
- Volume
- 25
- Issue
- 5
- Pages
- 882-893
- ISSN
- 2162-237X (ISSN-L)
- 2162-2388
- OpenAlex
- W1972809999
- Cited by
- 414
- References
- 48
- FWCI
- 45.89
- 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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Robust Adaptive Dynamic Programming and Feedback Stabilization of Nonlinear Systems. https://app.cochat.ai/papers/10.1109/tnnls.2013.2294968
- The paper
- Jiang, Y., & Jiang, Z.-P. (2014). Robust Adaptive Dynamic Programming and Feedback Stabilization of Nonlinear Systems. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/tnnls.2013.2294968
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.