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Cited by 294

The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.

Adaptive Dynamic Programming for Finite-Horizon Optimal Control of Discrete-Time Nonlinear Systems With $\varepsilon$-Error Bound

Fei-Yue Wang, Ning Jin, Derong Liu, Qinglai Wei·2011·IEEE Transactions on Neural Networks

Abstract

In this paper, we study the finite-horizon optimal control problem for discrete-time nonlinear systems using the adaptive dynamic programming (ADP) approach. The idea is to use an iterative ADP algorithm to obtain the optimal control law which makes the performance index function close to the greatest lower bound of all performance indices within an ε-error bound. The optimal number of control steps can also be obtained by the proposed ADP algorithms. A convergence analysis of the proposed ADP algorithms in terms of performance index function and control policy is made. In order to facilitate the implementation of the iterative ADP algorithms, neural networks are used for approximating the performance index function, computing the optimal control policy, and modeling the nonlinear system. Finally, two simulation examples are employed to illustrate the applicability of the proposed method.

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Metadata

Authors
Wang, F.-Y., Jin, N., Liu, D., & Wei, Q.
Venue
IEEE Transactions on Neural Networks
Published
2011-01
Volume
22
Issue
1
Pages
24-36
ISSN
  • 1045-9227 (ISSN-L)
  • 1941-0093
OpenAlex
W2159543578
Cited by
294
References
40
FWCI
32.71
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Adaptive Dynamic Programming for Finite-Horizon Optimal Control of Discrete-Time Nonlinear Systems With $\varepsilon$-Error Bound. https://app.cochat.ai/papers/10.1109/tnn.2010.2076370
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Wang, F.-Y., Jin, N., Liu, D., & Wei, Q. (2011). Adaptive Dynamic Programming for Finite-Horizon Optimal Control of Discrete-Time Nonlinear Systems With $\varepsilon$-Error Bound. IEEE Transactions on Neural Networks. https://doi.org/10.1109/tnn.2010.2076370

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