Robust Adaptive Dynamic Programming and Feedback Stabilization of Nonlinear Systems

Robust Adaptive Dynamic Programming and Feedback Stabilization of Nonlinear Systems. Jiang, Y. & Jiang, Z. IEEE Transactions on Neural Networks and Learning Systems, 25(5):882–893, May, 2014.

Paper doi abstract bibtex

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.

@article{jiang_robust_2014,
	title = {Robust {Adaptive} {Dynamic} {Programming} and {Feedback} {Stabilization} of {Nonlinear} {Systems}},
	volume = {25},
	issn = {2162-237X, 2162-2388},
	url = {https://ieeexplore.ieee.org/document/6701191/},
	doi = {10.1109/tnnls.2013.2294968},
	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.},
	language = {en},
	number = {5},
	urldate = {2022-02-03},
	journal = {IEEE Transactions on Neural Networks and Learning Systems},
	author = {Jiang, Yu and Jiang, Zhong-Ping},
	month = may,
	year = {2014},
	keywords = {/unread},
	pages = {882--893},
}

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