REFERENCES
1. Ma, S.; Guo, W.; Song, R.; Liu, Y. Unsupervised learning based coordinated multi-task allocation for unmanned surface vehicles. Neurocomputing 2021, 420, 227-45.
2. Wang, Q.; Liu, C.; Lan, J.; Ren, X.; Meng, Y.; Wang, X. Distributed secure surrounding control for multiple USVs against deception attacks: a Stackelberg game approach with reinforcement learning. IEEE. Trans. Intell. Veh. 2024, 1-12.
3. Zhao, J.; Wang, Y.; Cai, Z.; Liu, N.; Wu, K.; Wang, Y. Learning visual representation for autonomous drone navigation via a contrastive world model. IEEE. Trans. Artif. Intell. 2024, 5, 1263-76.
4. Guo, J.; Wang, X.; Xue, W.; Zhao, Y. System identification with binary-valued observations under data tampering attacks. IEEE. Trans. Autom. Control. 2021, 66, 3825-32.
5. Cui, Y.; Peng, L.; Li, H. Filtered probabilistic model predictive control-based reinforcement learning for unmanned surface vehicles. IEEE. Trans. Ind. Informat. 2022, 18, 6950-61.
6. Cui, Y.; Li, A.; Meng, X. A fault-tolerant control method for distributed flight control system facing wing damage. J. Syst. Eng. Electron. 2021, 32, 1041-52.
7. Qu, Y.; Cai, L. Nonlinear positioning control for underactuated unmanned surface vehicles in the presence of environmental disturbances. IEEE/ASME. Trans. Mechatronics. 2022, 27, 5381-91.
8. Peng, Z.; Wang, D.; Wang, J. Data-driven adaptive disturbance observers for model-free trajectory tracking control of maritime autonomous surface ships. IEEE. Tran. Neural. Netw. Learn. Syst. 2021, 32, 5584-94.
9. Xu, J.; Fang, H.; Zhang, B.; Guo, H. High-frequency square-wave signal injection based sensorless fault tolerant control for aerospace FTPMSM system in fault condition. IEEE. Trans. Transp. Electrification. 2022, 8, 4560-8.
10. Zhao, X.; Liu, C.; Zhao J. Adaptive sliding mode-based faulttolerant tracking control of multi-USV systems. In 2022 34th Chinese Control and Decision Conference (CCDC), Hefei, China, Aug 15-17, 2022; IEEE, 2022; pp 5980-5.
11. Yu, X. N.; Hao, L. Y.; Wang, X. L. Fault tolerant control for an unmanned surface vessel based on integral sliding mode state feedback control. Int. J. Control. Autom. Syst. 2022, 20, 2514-22.
12. Kebriaei, H.; Iannelli, L. Discrete-time robust hierarchical linear quadratic dynamic games. IEEE. Trans. Autom. Control. 2018, 63, 902-9.
13. Xu, Y.; Yang, H.; Jiang, B.; Polycarpou, M. M. Distributed optimal fault estimation and fault-tolerant control for interconnected systems: a Stackelberg differential graphical game approach. IEEE. Trans. Autom. Control. 2022, 67, 926-33.
14. Li, M.; Qin, J.; Ma, Q.; Zheng, W. X.; Kang, Y. Hierarchical optimal synchronization for linear systems via reinforcement learning: a Stackelberg–nash game perspective. IEEE. Trans. Autom. Control. 2021, 32, 1600-11.
15. Li, M.; Qin, J.; Freris, N. M.; Ho, D. W. C. Multiplayer stackelberg-Nash game for nonlinear system via value iteration-based integral reinforcement learning. IEEE. Trans. Neural. Netw. Learn. Syst. 2022, 33, 1429-40.
16. Chu, Z.; Wang, F.; Lei, T.; Luo, C. Path planning based on deep reinforcement learning for autonomous underwater vehicles under ocean current disturbance. IEEE. Trans. Intell. Veh. 2023, 8, 108-20.
17. Zhao, Y.; Ma, Y.; Hu, S. USV formation and path-following control via deep reinforcement learning with random braking. IEEE. Trans. Neural. Netw. Learn. Syst. 2021, 32, 5468-78.
18. Cui, X.; Wang, B.; Wang, L.; Chen, J. Online optimal learning algorithm for Stackelberg games with partially unknown dynamics and constrained inputs. Neurocomputing 2021, 445, 1-11.
