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
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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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
Reinforcement Learning: An Introduction
(2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 24,496
Bellman, R. E. (2010) · doi:10.1515/9781400835386 · Cited by 14,588
Watkins, C. (1989) · OpenGrey (Institut de l'Information Scientifique et Technique) · doi:10.17863/cam.132164 · Cited by 5,297
(2012) · Encyclopedia of the Sciences of Learning · doi:10.1007/978-1-4419-1428-6_5086 · Cited by 3,515
Applied Optimal Control: Optimization, Estimation, and Control
Bryson, A. E., Ho, Y.-C., & Siouris, G. M. (1979) · IEEE Transactions on Systems, Man, and Cybernetics · doi:10.1109/tsmc.1979.4310229 · Cited by 2,582
Convex Analysis and Optimization
Bertsekas, D. P., Nedić, A., & Ozdaglar, A. (2003) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 2,148
Reinforcement learning and adaptive dynamic programming for feedback control
Lewis, F. L., & Vrabie, D. (2009) · IEEE Circuits and Systems Magazine · doi:10.1109/mcas.2009.933854 · Cited by 1,558
Abu-Khalaf, M., & Lewis, F. L. (2005) · Automatica · doi:10.1016/j.automatica.2004.11.034 · Cited by 1,349
Prokhorov, D. V., & Wunsch, D. C. (1997) · IEEE Transactions on Neural Networks · doi:10.1109/72.623201 · Cited by 1,186
Cited by
Adaptive Dynamic Programming for Control: A Survey and Recent Advances
Liu, D., Xue, S., Zhao, B., Luo, B., & Wei, Q. (2021) · IEEE Transactions on Systems, Man, and Cybernetics: Systems · doi:10.1109/tsmc.2020.3042876 · Cited by 708
Self-citationValue 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
Liu, D., Wang, D., Wang, F.-Y., Li, H., & Yang, X. (2014) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2014.2357896 · Cited by 310
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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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- 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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