The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Model-Free Optimal Tracking Control via Critic-Only Q-Learning
Abstract
Model-free control is an important and promising topic in control fields, which has attracted extensive attention in the past few years. In this paper, we aim to solve the model-free optimal tracking control problem of nonaffine nonlinear discrete-time systems. A critic-only Q-learning (CoQL) method is developed, which learns the optimal tracking control from real system data, and thus avoids solving the tracking Hamilton-Jacobi-Bellman equation. First, the Q-learning algorithm is proposed based on the augmented system, and its convergence is established. Using only one neural network for approximating the Q-function, the CoQL method is developed to implement the Q-learning algorithm. Furthermore, the convergence of the CoQL method is proved with the consideration of neural network approximation error. With the convergent Q-function obtained from the CoQL method, the adaptive optimal tracking control is designed based on the gradient descent scheme. Finally, the effectiveness of the developed CoQL method is demonstrated through simulation studies. The developed CoQL method learns with off-policy data and implements with a critic-only structure, thus it is easy to realize and overcome the inadequate exploration problem.
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Cites
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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
Learning-Based Adaptive Optimal Tracking Control of Strict-Feedback Nonlinear Systems
Gao, W., & Jiang, Z.-P. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2761718 · Cited by 176
AFRL: Adaptive federated reinforcement learning for intelligent jamming defense in FANET
Mowla, N. I., Tran, N. H., Doh, I., & Chae, K. (2020) · Journal of Communications and Networks · doi:10.1109/jcn.2020.000015 · Cited by 146
Metadata
- Authors
- Luo, B., Liu, D., Huang, T., & Wang, D.
- Venue
- IEEE Transactions on Neural Networks and Learning Systems
- Published
- 2016-10
- Volume
- 27
- Issue
- 10
- Pages
- 2134-2144
- ISSN
- 2162-237X (ISSN-L)
- 2162-2388
- OpenAlex
- W2462102501
- Cited by
- 332
- References
- 6
- FWCI
- 31.26
- Type
- journal article
- Language
- en
- Open access
- closed
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