The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Adaptive Neural Network Control of AUVs With Control Input Nonlinearities Using Reinforcement Learning
Open accessAbstract
In this paper, we investigate the trajectory tracking problem for a fully actuated autonomous underwater vehicle (AUV) that moves in the horizontal plane. External disturbances, control input nonlinearities and model uncertainties are considered in our control design. Based on the dynamics model derived in the discrete-time domain, two neural networks (NNs), including a critic and an action NN, are integrated into our adaptive control design. The critic NN is introduced to evaluate the long-time performance of the designed control in the current time step, and the action NN is used to compensate for the unknown dynamics. To eliminate the AUV's control input nonlinearities, a compensation item is also designed in the adaptive control. Rigorous theoretical analysis is performed to prove the stability and performance of the proposed control law. Moreover, the robustness and effectiveness of the proposed control method are tested and validated through extensive numerical simulation results.
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Continuous control with deep reinforcement learning
Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., & Wierstra, D. (2015) · arXiv · doi:10.48550/arxiv.1509.02971 · Cited by 10,782
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
Narendra, K. S., & Annaswamy, A. M. (1989) · Cited by 3,988
Adaptive Neural Network Control of an Uncertain Robot With Full-State Constraints
He, W., Chen, Y., & Yin, Z. (2016) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2015.2411285 · Cited by 1,273
Prokhorov, D. V., & Wunsch, D. C. (1997) · IEEE Transactions on Neural Networks · doi:10.1109/72.623201 · Cited by 1,186
Liu, Y.-J., & Tong, S. (2017) · Automatica · doi:10.1016/j.automatica.2016.10.011 · Cited by 828
Adaptive Neural Impedance Control of a Robotic Manipulator With Input Saturation
He, W., Dong, Y., & Sun, C. (2016) · IEEE Transactions on Systems, Man, and Cybernetics: Systems · doi:10.1109/tsmc.2015.2429555 · Cited by 790
Online learning control by association and reinforcement
Si, J., & Wang, Y.-T. (2001) · IEEE Transactions on Neural Networks · doi:10.1109/72.914523 · Cited by 773
Adaptive dynamic surface control of nonlinear systems with unknown dead zone in pure feedback form
Zhang, T. P., & Ge, S. S. (2008) · Automatica · doi:10.1016/j.automatica.2007.11.025 · Cited by 752
Adaptive Neural Network Control of Robotic Manipulators
Ge, S. S., Lee, T. H., & Harris, C. J. (1998) · World Scientific Series in Robotics and Intelligent Systems · doi:10.1142/3774 · Cited by 618
Cited by
Actor-Critic Deep Reinforcement Learning for Solving Job Shop Scheduling Problems
Liu, C.-L., Chang, C.-C., & Tseng, C.-J. (2020) · IEEE Access · doi:10.1109/access.2020.2987820 · Cited by 281
Deep reinforcement learning-based controller for path following of an unmanned surface vehicle
Woo, J., Yu, C., & Kim, N. (2019) · Ocean Engineering · doi:10.1016/j.oceaneng.2019.04.099 · Cited by 246
Metadata
- Authors
- Cui, R., Yang, C., Li, Y., & Sharma, S.
- Venue
- IEEE Transactions on Systems, Man, and Cybernetics: Systems
- Published
- 2017-06
- Volume
- 47
- Issue
- 6
- Pages
- 1019-1029
- ISSN
- 2168-2216 (ISSN-L)
- 2168-2232
- OpenAlex
- W2568019971
- Cited by
- 538
- References
- 38
- FWCI
- 34.27
- Type
- journal article
- Language
- en
- Open access
- green
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