The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Policy Gradient Adaptive Dynamic Programming for Data-Based Optimal Control
Abstract
The model-free optimal control problem of general discrete-time nonlinear systems is considered in this paper, and a data-based policy gradient adaptive dynamic programming (PGADP) algorithm is developed to design an adaptive optimal controller method. By using offline and online data rather than the mathematical system model, the PGADP algorithm improves control policy with a gradient descent scheme. The convergence of the PGADP algorithm is proved by demonstrating that the constructed Q -function sequence converges to the optimal Q -function. Based on the PGADP algorithm, the adaptive control method is developed with an actor-critic structure and the method of weighted residuals. Its convergence properties are analyzed, where the approximate Q -function converges to its optimum. Computer simulation results demonstrate the effectiveness of the PGADP-based adaptive control method.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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: A Survey
Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996) · Journal of Artificial Intelligence Research · doi:10.1613/jair.301 · Cited by 8,972
Al-Tamimi, A., Lewis, F. L., & Abu-Khalaf, M. (2007) · Automatica · doi:10.1016/j.automatica.2006.09.019 · Cited by 611
Self-citationLewis, 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
Self-citationNeural Network-Based Event-Triggered State Feedback Control of Nonlinear Continuous-Time Systems
Sahoo, A., Xu, H., & Jagannathan, S. (2016) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2015.2416259 · Cited by 292
Cited by
Nguyen, T. T., Nguyen, N. D., & Nahavandi, S. (2020) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2020.2977374 · Cited by 1,306
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-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
- Luo, B., Liu, D., Wu, H.-N., Wang, D., & Lewis, F. L.
- Venue
- IEEE Transactions on Cybernetics
- Published
- 2017-10
- Volume
- 47
- Issue
- 10
- Pages
- 3341-3354
- ISSN
- 2168-2267 (ISSN-L)
- 2168-2275
- OpenAlex
- W2550394442
- Cited by
- 222
- References
- 5
- FWCI
- 16.97
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Policy Gradient Adaptive Dynamic Programming for Data-Based Optimal Control. https://app.cochat.ai/papers/10.1109/tcyb.2016.2623859
- The paper
- Luo, B., Liu, D., Wu, H.-N., Wang, D., & Lewis, F. L. (2017). Policy Gradient Adaptive Dynamic Programming for Data-Based Optimal Control. IEEE Transactions on Cybernetics. https://doi.org/10.1109/tcyb.2016.2623859
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
