The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Finite-Approximation-Error-Based Optimal Control Approach for Discrete-Time Nonlinear Systems
Abstract
In this paper, a new iterative adaptive dynamic programming (ADP) algorithm is developed to solve optimal control problems for infinite-horizon discrete-time nonlinear systems with finite approximation errors. The idea is to use an iterative ADP algorithm to obtain the iterative control law that makes the iterative performance index function reach the optimum. When the iterative control law and the iterative performance index function in each iteration cannot be accurately obtained, the convergence conditions of the iterative ADP algorithm are obtained. When convergence conditions are satisfied, it is shown that the iterative performance index functions can converge to a finite neighborhood of the greatest lower bound of all performance index functions under some mild assumptions. Neural networks are used to approximate the performance index function and compute the optimal control policy, respectively, for facilitating the implementation of the iterative ADP algorithm. Finally, two simulation examples are given to illustrate the performance of the present method.
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Cites
Murray, J. J., Cox, C. J., Lendaris, G. G., & Saeks, R. (2002) · IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews) · doi:10.1109/tsmcc.2002.801727 · Cited by 677
Lewis, F. L., & Vamvoudakis, K. G. (2011) · IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) · doi:10.1109/tsmcb.2010.2043839 · Cited by 544
Vamvoudakis, K. G., & Lewis, F. L. (2011) · Automatica · doi:10.1016/j.automatica.2011.03.005 · Cited by 525
A Self-Learning Call Admission Control Scheme for CDMA Cellular Networks
Liu, D., Zhang, Y., & Zhang, H. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2005.853408 · Cited by 162
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Modares, H., Lewis, F. L., & Naghibi-Sistani, M.-B. (2013) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2013.2276571 · Cited by 462
Xu, B., Yang, C., & Pan, Y. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2015.2456972 · Cited by 347
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
- Liu, D., & Wei, Q.
- Venue
- IEEE Transactions on Cybernetics
- Published
- 2013-04
- Volume
- 43
- Issue
- 2
- Pages
- 779-789
- ISSN
- 2168-2267 (ISSN-L)
- 2168-2275
- OpenAlex
- W1983089547
- Cited by
- 304
- References
- 4
- FWCI
- 38.69
- Type
- journal article
- Language
- en
- Open access
- closed
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- Liu, D., & Wei, Q. (2013). Finite-Approximation-Error-Based Optimal Control Approach for Discrete-Time Nonlinear Systems. IEEE Transactions on Cybernetics. https://doi.org/10.1109/tsmcb.2012.2216523
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