The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Finite-Approximation-Error-Based Discrete-Time Iterative Adaptive Dynamic Programming
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. First, a new generalized value iteration algorithm of ADP is developed to make the iterative performance index function converge to the solution of the Hamilton-Jacobi-Bellman equation. The generalized value iteration algorithm permits an arbitrary positive semi-definite function to initialize it, which overcomes the disadvantage of traditional value iteration algorithms. When the iterative control law and iterative performance index function in each iteration cannot accurately be obtained, for the first time a new "design method of the convergence criteria" for the finite-approximation-error-based generalized value iteration algorithm is established. A suitable approximation error can be designed adaptively to make the iterative performance index function converge to a finite neighborhood of the optimal performance index function. Neural networks are used to implement the iterative ADP algorithm. Finally, two simulation examples are given to illustrate the performance of the developed 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
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
Self-citation
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-citationLiu, Y.-J., Li, J., Tong, S., & Chen, C. L. P. (2016) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2015.2508926 · Cited by 517
Value Iteration Adaptive Dynamic Programming for Optimal Control of Discrete-Time Nonlinear Systems
Wei, Q., Liu, D., & Lin, H. (2016) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2015.2492242 · Cited by 465
Self-citationLiu, D., Yang, X., Wang, D., & Wei, Q. (2015) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2015.2417170 · Cited by 440
Self-citationAdaptive Controller Design-Based ABLF for a Class of Nonlinear Time-Varying State Constraint Systems
Liu, Y.-J., Lu, S., Li, D., & Tong, S. (2017) · IEEE Transactions on Systems, Man, and Cybernetics: Systems · doi:10.1109/tsmc.2016.2633007 · Cited by 284
Metadata
- Authors
- Wei, Q., Wang, F.-Y., Liu, D., & Yang, X.
- Venue
- IEEE Transactions on Cybernetics
- Published
- 2014-12
- Volume
- 44
- Issue
- 12
- Pages
- 2820-2833
- ISSN
- 2168-2267 (ISSN-L)
- 2168-2275
- OpenAlex
- W2011310556
- Cited by
- 188
- References
- 3
- FWCI
- 34.16
- Type
- journal article
- Language
- en
- Open access
- closed
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- Wei, Q., Wang, F.-Y., Liu, D., & Yang, X. (2014). Finite-Approximation-Error-Based Discrete-Time Iterative Adaptive Dynamic Programming. IEEE Transactions on Cybernetics. https://doi.org/10.1109/tcyb.2014.2354377
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