The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Reinforcement Learning and Feedback Control: Using Natural Decision Methods to Design Optimal Adaptive Controllers
Abstract
This article describes the use of principles of reinforcement learning to design feedback controllers for discrete- and continuous-time dynamical systems that combine features of adaptive control and optimal control. Adaptive control [1], [2] and optimal control [3] represent different philosophies for designing feedback controllers. Optimal controllers are normally designed of ine by solving Hamilton JacobiBellman (HJB) equations, for example, the Riccati equation, using complete knowledge of the system dynamics. Determining optimal control policies for nonlinear systems requires the offline solution of nonlinear HJB equations, which are often difficult or impossible to solve. By contrast, adaptive controllers learn online to control unknown systems using data measured in real time along the system trajectories. Adaptive controllers are not usually designed to be optimal in the sense of minimizing user-prescribed performance functions. Indirect adaptive controllers use system identification techniques to first identify the system parameters and then use the obtained model to solve optimal design equations [1]. Adaptive controllers may satisfy certain inverse optimality conditions [4].
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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
Watkins, C. J. C. H., & Dayan, P. (1992) · Machine Learning · doi:10.1007/bf00992698 · Cited by 9,083
Lewis, F. L., Vrabie, D. L., & Syrmos, V. L. (2012) · doi:10.1002/9781118122631 · Cited by 2,020
Self-citationMurray, 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
Self-citationVamvoudakis, K. G., & Lewis, F. L. (2011) · Automatica · doi:10.1016/j.automatica.2011.03.005 · Cited by 525
Self-citationHewer, G. (1971) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1971.1099755 · Cited by 326
Cited by
Value iteration and adaptive dynamic programming for data-driven adaptive optimal control design
Bian, T., & Jiang, Z.-P. (2016) · Automatica · doi:10.1016/j.automatica.2016.05.003 · Cited by 348
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
Gao, W., Jiang, Y., Jiang, Z.-P., & Chai, T. (2016) · Automatica · doi:10.1016/j.automatica.2016.05.008 · Cited by 234
Neural Operators for Bypassing Gain and Control Computations in PDE Backstepping
Bhan, L., Shi, Y., & Krstic, M. (2024) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2023.3347499 · Cited by 58
Zheng, M., Wu, Y., & Li, C. (2021) · Aerospace Science and Technology · doi:10.1016/j.ast.2021.107126 · Cited by 37
Metadata
- Venue
- IEEE Control Systems
- Published
- 2012-12
- Volume
- 32
- Issue
- 6
- Pages
- 76-105
- ISSN
- 1066-033X (ISSN-L)
- 1941-000X
- OpenAlex
- W2152161277
- Cited by
- 1,105
- References
- 7
- FWCI
- 40.73
- Type
- journal article
- Language
- en
- Open access
- closed
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