The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Policy Iteration Adaptive Dynamic Programming Algorithm for Discrete-Time Nonlinear Systems
Abstract
This paper is concerned with a new discrete-time policy iteration adaptive dynamic programming (ADP) method for solving the infinite horizon optimal control problem of nonlinear systems. The idea is to use an iterative ADP technique to obtain the iterative control law, which optimizes the iterative performance index function. The main contribution of this paper is to analyze the convergence and stability properties of policy iteration method for discrete-time nonlinear systems for the first time. It shows that the iterative performance index function is nonincreasingly convergent to the optimal solution of the Hamilton-Jacobi-Bellman equation. It is also proven that any of the iterative control laws can stabilize the nonlinear systems. Neural networks are used to approximate the performance index function and compute the optimal control law, respectively, for facilitating the implementation of the iterative ADP algorithm, where the convergence of the weight matrices is analyzed. Finally, the numerical results and analysis are presented to illustrate the performance of the developed method.
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Cites
Bellman, R. E. (2010) · doi:10.1515/9781400835386 · Cited by 14,588
Spacecraft Attitude Determination and Control
(1978) · Astrophysics and Space Science Library · doi:10.1007/978-94-009-9907-7 · Cited by 1,892
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A Self-Learning Call Admission Control Scheme for CDMA Cellular Networks
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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-citationLiu, Y.-J., Gao, Y., Tong, S., & Li, Y. (2016) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2015.2418000 · Cited by 408
Off-Policy Reinforcement Learning for $ H_\infty $ Control Design
Luo, B., Wu, H.-N., & Huang, T. (2015) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2014.2319577 · Cited by 362
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
Self-citationGao, W., Jiang, Y., Jiang, Z.-P., & Chai, T. (2016) · Automatica · doi:10.1016/j.automatica.2016.05.008 · Cited by 234
Metadata
- Authors
- Liu, D., & Wei, Q.
- Venue
- IEEE Transactions on Neural Networks and Learning Systems
- Published
- 2014-03
- Volume
- 25
- Issue
- 3
- Pages
- 621-634
- ISSN
- 2162-237X (ISSN-L)
- 2162-2388
- OpenAlex
- W2048687352
- Cited by
- 753
- References
- 4
- FWCI
- 42.84
- Type
- journal article
- Language
- en
- Open access
- closed
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