Value iteration and adaptive dynamic programming for data-driven adaptive optimal control design
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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].”
From Data-driven control of hydraulic servo actuator: An event-triggered adaptive dynamic programming approach (2023) · cites this paper as [26]
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Learning-Based Adaptive Optimal Tracking Control of Strict-Feedback Nonlinear Systems
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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
- bronze
- doi.org
- Licence
- 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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