REFERENCES

1. Olfati-Saber, R.; Fax, J. A.; Murray, R. M. Consensus and cooperation in networked multi-agent systems. Proc. IEEE. 2007, 95, 215-33.

2. Liang, Z.; Cao, J.; Jiang, S.; Xu, H. Hierarchical reinforcement learning with partner modeling for distributed multi-agent cooperation. IEEE. Trans. Parallel. Distrib. Syst. 2024, 1-13.

3. Peng, C.; Zhang, A.; Li, J. Neuro-adaptive cooperative control for high-order nonlinear multi-agent systems with uncertainties. Int. J. Appl. Math. Comput. Sci. 2021, 31, 635-45.

4. An, R.; Wang, Y.; Zhao, Y.; Zhang, J. Consensus of high-order multiagent systems with binary-valued communications and switching topologies. IEEE. Trans. Control. Netw. Syst. 2025, 12, 1369-80.

5. Li, B.; Zhao, L.; Wen, S. Periodic event-triggered consensus of stochastic multi-agent systems under switching topology. AI. Sci. Eng. 2025, 1, 147-56.

6. He, W.; Chen, X.; Zhang, M.; Sun, Y.; Sekiguchi, A.; She, J. Data-driven optimal consensus control for switching multiagent systems via joint communication graph. IEEE. Trans. Ind. Inf. 2024, 20, 5959-68.

7. Zhang, L.; Li, Y.; Ahn, C. K.; Liang, H. Adaptive temporal partitioning for communication-efficient consensus in nonlinear multiagent systems. IEEE. Trans. Ind. Informat. 2026, 1-12.

8. Jin, N.; Xu, J.; Zhang, H. Distributed optimal consensus control of multiagent systems involving state and control dependent multiplicative noise. IEEE. Trans. Autom. Control. 2023, 68, 7787-94.

9. Wan, X.; Guo, Y.; Wu, X. Differentially private consensus for multi-agent systems under switching topology. IEEE. Trans. Circuits. Syst. II. 2023, 70, 3499-503.

10. Xing, W.; Zhao, X.; Basar, T.; Xia, W. Security investment in cyber-physical systems: stochastic games with asymmetric information and resource-constrained players. IEEE. Trans. Autom. Control. 2022, 67, 5384-91.

11. Yang, C.; Zhang, X.; Zhang, L.; et al. Coordinated energy-efficient walking assistance for paraplegic patients by using the exoskeleton-walker system. Intell. Robot. 2024, 4, 107-24.

12. Yang, A.; Liang, X.; Zhang, J.; Hou, Y.; Wang, N. Global optimal consensus for UAV swarms with time-varying objective functions and bounded input constraints. IEEE. Trans. Aerosp. Electron. Syst. 2024, 60, 3822-36.

13. Jia, C.; Chen, F. Feasibility of optimal formation under multiplicative noise with energy and time constraints. IEEE. Trans. Autom. Sci. Eng. 2025, 22, 18522-30.

14. Shen, H.; Wang, Y.; Wang, J.; Park, J. H. A fuzzy-model-based approach to optimal control for nonlinear Markov jump singularly perturbed systems: a novel integral reinforcement learning scheme. IEEE. Trans. Fuzzy. Syst. 2023, 31, 3734-40.

15. Tao, Y. Y.; Che, W. W.; Wu, Z. G.; Xu, Y.; Dong, S. Asynchronous control of 2-D Markov jump roesser systems with nonideal transition probabilities. IEEE. Trans. Cybern. 2024, 54, 6643-52.

16. Zhong, X.; He, H.; Zhang, H.; Wang, Z. Optimal control for unknown discrete-time nonlinear Markov jump systems using adaptive dynamic programming. IEEE. Trans. Neural. Netw. Learn. Syst. 2014, 25, 2141-55.

17. Feng, L.; Yang, G. Adaptive memory-event-triggered fault detection for interval type-2 fuzzy Markov jump systems with sensor saturation. IEEE. Trans. Fuzzy. Syst. 2024, 32, 2089-101.

18. Wang, X.; Park, J. H.; Yang, H. An improved protocol to consensus of delayed MASs with UNMS and aperiodic DoS cyber-attacks. IEEE. Trans. Netw. Sci. Eng. 2021, 8, 2506-16.

19. Wang, J.; Dong, J. Robust predictive bipartite bumpless control for Markov jump MASs under switching topology: application to rotary-wing air vehicles. IEEE. Trans. Veh. Technol. 2025, 74, 7020-32.

20. Elhamamsy, A.; Aghili, F.; Aghdam, A. G. Connectivity preservation and collision avoidance in multi-agent systems using model predictive control. IEEE. Trans. Netw. Sci. Eng. 2023, 10, 1779-91.

21. Yao, J.; Li, X.; Zhang, Y.; Ji, J.; Ren, G.; Ren, Z. Obstacle avoidance control for unmanned tracked vehicle formation with communication constraints. IEEE. Trans. Autom. Sci. Eng. 2025, 22, 19735-49.

22. Shang, Y. Consensus of noisy multiagent systems with Markovian switching topologies and time-varying delays. Math. Probl. Eng. 2015, 2015, 1-13.

23. Ji, L.; Wang, C.; Zhang, C.; Wang, H.; Li, H. Optimal consensus model-free control for multi-agent systems subject to input delays and switching topologies. Inf. Sci. 2022, 589, 497-515.

24. Han, X.; Zhao, X.; Karimi, H. R.; Wang, D.; Zong, G. Adaptive optimal control for unknown constrained nonlinear systems with a novel quasi-model network. IEEE. Trans. Neural. Netw. Learn. Syst. 2022, 33, 2867-78.

25. Chen, C.; Lewis, F. L.; Li, B. Homotopic policy iteration-based learning design for unknown linear continuous-time systems. Automatica 2022, 138, 110153.

26. Cai, Y.; Zhang, H.; Zhang, K.; Liu, C. Fuzzy adaptive dynamic programming-based optimal leader-following consensus for heterogeneous nonlinear multi-agent systems. Neural. Comput. Appl. 2020, 32, 8763-81.

27. Yang, H.; Zhang, H.; Wang, Z.; Huang, C.; Yan, H. Fault-tolerant optimal consensus for multiagent systems: a fuzzy-based game approach. IEEE. Trans. Aerosp. Electron. Syst. 2024, 60, 6839-52.

28. Peng, Z.; Zhao, Y.; Hu, J.; Ghosh, B. K. Data-driven optimal tracking control of discrete-time multi-agent systems with two-stage policy iteration algorithm. Inf. Sci. 2019, 481, 189-202.

29. Vrabie, D.; Pastravanu, O.; Abu-Khalaf, M.; Lewis, F. Adaptive optimal control for continuous-time linear systems based on policy iteration. Automatica 2009, 45, 477-84.

30. Wang, D.; Ren, J.; Ha, M.; Qiao, J. System stability of learning-based linear optimal control with general discounted value iteration. IEEE. Trans. Neural. Netw. Learn. Syst. 2023, 34, 6504-14.

31. Ren, Y.; Wang, Q.; Duan, Z. Optimal leader-following consensus control of multi-agent systems: a neural network based graphical game approach. IEEE. Trans. Netw. Sci. Eng. 2022, 9, 3590-601.

32. Zhang, H.; Zhang, J.; Yang, G.; Luo, Y. Leader-based optimal coordination control for the consensus problem of multiagent differential games via fuzzy adaptive dynamic programming. IEEE. Trans. Fuzzy. Syst. 2015, 23, 152-63.

33. Lewis, F. L.; Vrabie, D. L.; Syrmos, V. L. Optimal control. Wiley; 2012.

34. Zhang, H.; Jiang, H.; Luo, Y.; Xiao, G. Data-driven optimal consensus control for discrete-time multi-agent systems with unknown dynamics using reinforcement learning method. IEEE. Trans. Ind. Electron. 2017, 64, 4091-100.

35. Ding, J.; Sheng, J.; Li, F.; Xu, J.; Ling, Q. Consensus of multi-agent Markovian jump systems under event-triggered control. In 2023 42nd Chinese Control Conference (CCC), Tianjin, China. Jul 24-26, 2023. IEEE; 2023. pp. 5682-6.

36. Yao, Z.; Zhu, Q.; Qin, P.; Luo, M. Homotopic reinforcement learning for distributed consensus control of stochastic Markov jump multi-agent systems. J. Supercomput. 2025, 81, 7885.

37. Michael, N.; Schwager, M.; Kumar, V.; Rus, D. An experimental study of time scales and stability in networked multi-robot systems. In Experimental robotics; Khatib, O.; Kumar, V.; Sukhatme, G.; Eds.; Berlin: Springer Berlin Heidelberg; 2014. pp. 631-43.

38. Wang, X.; Yang, H.; Zhong, S. Reliable asynchronous control for delayed multi-agent systems with stochastic faults and switching topologies. Int. J. Syst. Sci. 2021, 52, 3214-25.

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