Cited by 348Adaptive Dynamic Programming ControlAdvanced Control Systems OptimizationReinforcement Learning in Robotics

Value iteration and adaptive dynamic programming for data-driven adaptive optimal control design

Open access
Bian, Tao, Jiang, Zhong-Ping·2016·Automatica

Integrity signals

Registry

Crossref · 10.1016/j.automatica.2016.05.003

Cites

88 of 88 references verified0 citation errors · 0 not checkable

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  • ADP is an effective way to achieve high performance of the optimal controller and relies on adaptive control, optimal control and reinforcement learning.

    “Adaptive dynamic programming (ADP) ensures an e ective way to achieve high performance of the optimal controller which relies on adaptive control, optimal control and reinforcement learning [9, 23-26].”

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Authors
Bian, T., & Jiang, Z.-P.
Affiliations
New York University (US)
Corresponding author
  • Bian, Tao
  • Jiang, Zhong-Ping
Venue
Automatica
Publisher
Elsevier BV
Published
2016-09
Volume
71
Pages
348-360
ISSN
0005-1098 (ISSN-L)
OpenAlex
W2467518411
Cited by
348
References
88
Citations by year
2026: 52 · 2025: 70 · 2024: 53 · 2023: 37 · 2022: 30 · 2021: 26 · 2020: 20 · 2019: 33 · 2018: 12 · 2017: 10 · 2016: 2 · 2015: 2 · 2014: 1
FWCI
15.43
Type
journal article
Language
en
Open access
Locations
Automatica (acceptedVersion)
Funders
National Science Foundation (ECCS-1230040, ECCS-1501044)
Topics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Adaptive Dynamic Programming Control
  • Physical Sciences › Engineering › Control and Systems Engineering › Advanced Control Systems Optimization
  • Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
Keywords
value iteration, adaptive dynamic programming, data-driven control, adaptive optimal control, non-model-based control, continuous-time linear systems, unknown dynamics, stochastic approximation, convergence analysis, policy iteration

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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.

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