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Cited by 414

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

Jiang, Yu, Jiang, Zhong-Ping·2014·IEEE Transactions on Neural Networks and Learning 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.

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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

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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

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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.