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Review Article
A Unified Mathematical Framework for Adaptive, Fuzzy and Learning-Based Consensus Control of Uncertain Nonlinear Multi-Agent Systems
Shivdeep Kaur1
1 Assistant Professor, Mata Gujri College, Fatehgarh Sahib, Punjab, India.
Published Online: November-December 2025
Pages: 42-56
Cite this article
No DOIReferences
1. Y. Jiang, B. Niu, G. Zong, X. Zhao, and P. Zhao, “Distributed adaptive secure consensus tracking control for asynchronous switching nonlinear MASs under sensor attacks and actuator faults,” IEEE Trans. Autom. Sci. Eng., vol. 21, no. 3, pp. 3253–3263, Jul. 2024.
2. B. Ning, Q.-L. Han, Z. Zuo, L. Ding, Q. Lu, and X. Ge, “Fixed-time and prescribed-time consensus control of multiagent systems and its applications: A survey of recent trends and methodologies,” IEEE Trans. Ind. Informat., vol. 19, no. 2, pp. 1121–1135, Feb. 2023.
3. J. Wang, J. Liu, Y. Li, C. L. P. Chen, Z. Liu, and F. Li, “Prescribed time fuzzy adaptive consensus control for multiagent systems with dead-zone input and sensor faults,” IEEE Trans. Autom. Sci. Eng., vol. 21, no. 3, pp. 4016–4027, Jul. 2024.
4. G. Wen and C. L. P. Chen, “Optimized backstepping consensus control using reinforcement learning for a class of nonlinear strict-feed back dynamic multi-agent systems,” IEEE Trans. Neural Netw. Learn. Syst., vol. 34, no. 3, pp. 1524–1536, Mar. 2023.
5. Q. Wei, X. Wang, X. Zhong, and N. Wu, “Consensus control of leader–following multi-agent systems in directed topology with heterogeneous disturbances,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 2, pp. 423–431, Feb. 2021.
6. M. Lv,W. Yu, J. Cao, and S. Baldi, “Consensus in high-power multiagent systems with mixed unknown control directions via hybrid Nussbaumbased control,” IEEE Trans. Cybern., vol. 52, no. 6, pp. 5184–5196, Jun. 2022.
7. D. Deng, P. He, P. Shi, and L. Kovács, “Distributed consensus of nonlinear stochastic multi-agent systems with input and output delays via predictive control,” Nonlinear Dyn., vol. 112, no. 23, pp. 21227–21239, Aug. 2024.
8. Y. Yang, Y. Pan, C. Z. Xu, and D. C. Wunsch, “Hamiltonian-driven adaptive dynamic programming with efficient experience replay,” IEEE Trans. Neural Netw. Learn. Syst., vol. 35, no. 3, pp. 3278–3290, Mar. 2024.
9. Z. Zhang, K. Zhang, X. Xie, and V. Stojanovic, “ADP-based prescribed time control for nonlinear time-varying delay systems with uncertain parameters,” IEEE Trans. Autom. Sci. Eng., vol. 22, pp. 3086–3096, Apr. 2024, doi: 10.1109/TASE.2024.3389020.
10. Q. Zhu, “Event-triggered sampling problem for exponential stability of stochastic nonlinear delay systems driven by Lévy processes,” IEEE Trans. Autom. Control, vol. 70, no. 2, pp. 1176–1183, Feb. 2025.
11. G. Zong, Y. Wang, B. Niu, S.-F. Su, and K. Shi, “Event-triggered adaptive NN tracking control for nonlinear systems with asymmetric time-varying output constraints and application to an AUVs,” IEEE Trans. Veh. Technol., vol. 74, no. 1, pp. 413–424, Jan. 2025.
12. G. Zhang and Q. Zhu, “Event-triggered optimal control for nonlinearstochastic systems via adaptive dynamic programming,” Nonlinear Dyn., vol. 105, no. 1, pp. 387–401, 2021.
13. Y. Wang and G. Zong, “Dynamic event-triggered adaptive fixedtime practical tracking control for nonlinear systems through funnel function,” IEEE Trans. Autom. Sci. Eng., pp. 1–10, Sep. 2024, doi: 10.1109/TASE.2024.3458176.
14. X. Wang, T. Wang, and D. Ding, “Barrier-function-enabled control for vessel systems under dynamic event-triggered protocols: The ADP approach,” IEEE Trans. Intell. Vehicles, pp. 1–10, Aug. 2024, doi: 10.1109/TIV.2024.3451517.
15. B. Li, N. Chen, B. Luo, J. Chen, C. Yang, and W. Gui, “ADP-based event-triggered constrained optimal control on spatiotemporal process: Application to temperature field in roller kiln,” IEEE Trans. Neural Netw. Learn. Syst., vol. 35, no. 3, pp. 3229–3241, Mar. 2024.
16. J. Wang, Z. Zhang, B. Tian, and Q. Zong, “Event-based robust optimalconsensus control for nonlinear multiagent system with local adaptive dynamic programming,” IEEE Trans. Neural Netw. Learn. Syst., vol. 35, no. 1, pp. 1073–1086, Jan. 2024.
17. H. Fu, H. He, and Y. Chen, “Event-triggered cooperative tracking control of multi agent systems with a dynamic leader via approximate dynamic programming,” IEEE Trans. Artif. Intell., vol. 5, no. 6, pp. 2752–2765, Jun. 2024.
18. K. Ding and Q. Zhu, “Fuzzy model-based quantitative control for prefixed time synchronization of stochastic reaction-diffusion complex networks under cyber-attacks,” IEEE Trans. Autom. Sci. Eng., vol. 21, no. 4, pp. 6693–6707, Oct. 2024.
19. G. Zong, H. Xie, D. Yang, X. Zhao, and Y. Yi, “Adaptive fuzzy tracking control for switched nonlinear systems under FDI attacks and input saturation: A flexible transient performance approach,” IEEE Trans. Cybern., vol. 54, no. 12, pp. 7479–7488, Dec. 2024.
20. G. Wen, C. L. P. Chen, S. S. Ge, H. Yang, and X. Liu, “Optimized adaptive nonlinear tracking control using actor–critic reinforcement learning strategy,” IEEE Trans. Ind. Informat., vol. 15, no. 9, pp. 4969–4977, Sep. 2019.
21. K. Sun, S. Sui, and S. Tong, “Fuzzy adaptive decentralized optimal control for strict feedback nonlinear large-scale systems,” IEEE Trans. Cybern., vol. 48, no. 4, pp. 1326–1339, Apr. 2018.
22. H. Zhang, X. Zhao, H. Wang, G. Zong, and N. Xu, “Hierarchical sliding mode surface-based adaptive actor–critic optimal control for switched nonlinear systems with unknown perturbation,” IEEE Trans. Neural Netw. Learn. Syst., vol. 35, no. 2, pp. 1559–1571, Feb. 2024.
23. G. Zhang and Q. Zhu, “State and output feedback’s finite-time guaranteed cost H∞ control for uncertain nonlinear stochastic systems with time-varying delays,” J. Franklin Inst., vol. 360, no. 12, pp. 8037–8061, Aug. 2023.
24. F. Jia and X. He, “Adaptive fault-tolerant tracking control for discrete time non strict-feedback nonlinear systems with stochastic noises,” IEEE Trans. Autom. Sci. Eng., vol. 21, no. 3, pp. 3344–3356, Jul. 2024.
25. G. Zhang and Q. Zhu, “Finite-time guaranteed cost control for uncertain delayed switched nonlinear stochastic systems,” J. Franklin Inst., vol. 359, no. 16, pp. 8802–8818, Nov. 2022.
26. K. Li and Y. Li, “Adaptive NN optimal consensus fault-tolerant control for stochastic nonlinear multiagent systems,” IEEE Trans. Neural Netw. Learn. Syst., vol. 34, no. 2, pp. 947–957, Feb. 2023.
27. Jiang Y, Niu B, Zong G, Zhao X, Zhao P. Distributed adaptive secure consensus tracking control for asynchronous switching nonlinear MASs under sensor attacks and actuator faults. IEEE Trans. Autom. Sci. Eng. 2024;21(3):3253–3263.
28. Ning B, Han QL, Zuo Z, Ding L, Lu Q, Ge X. Fixed-time and prescribed-time consensus control of multiagent systems and its applications: A survey of recent trends and methodologies. IEEE Trans. Ind. Informat. 2023;19(2):1121–1135.
29. Wang J, Liu J, Li Y, Chen CLP, Liu Z, Li F. Prescribed time fuzzy adaptive consensus control for multiagent systems with dead-zone input and sensor faults. IEEE Trans. Autom. Sci. Eng. 2024;21(3):4016–4027.
30. Wen G, Chen CLP. Optimized backstepping consensus control using reinforcement learning for a class of nonlinear strict-feedback dynamic multi-agent systems. IEEE Trans. Neural Netw. Learn. Syst. 2023;34(3):1524–1536.
31. Wei Q, Wang X, Zhong X, Wu N. Consensus control of leader–following multi-agent systems in directed topology with heterogeneous disturbances. IEEE/CAA J. Autom. Sinica 2021;8(2):423–431.
32. Lv M, Yu W, Cao J, Baldi S. Consensus in high-power multiagent systems with mixed unknown control directions via hybrid Nussbaum-based control. IEEE Trans. Cybern. 2022;52(6):5184–5196.
33. Deng D, He P, Shi P, Kovács L. Distributed consensus of nonlinear stochastic multi-agent systems with input and output delays via predictive control. Nonlinear Dyn. 2024;112(23):21227–21239.
34. Yang Y, Pan Y, Xu CZ, Wunsch DC. Hamiltonian-driven adaptive dynamic programming with efficient experience replay. IEEE Trans. Neural Netw. Learn. Syst. 2024;35(3):3278–3290.
35. Zhang Z, Zhang K, Xie X, Stojanovic V. ADP-based prescribed time control for nonlinear time-varying delay systems with uncertain parameters. IEEE Trans. Autom. Sci. Eng. 2024;22(—):3086–3096.
36. Zhu Q. Event-triggered sampling problem for exponential stability of stochastic nonlinear delay systems driven by Lévy processes. IEEE Trans. Autom. Control 2025;70(2):1176–1183.
37. Zong G, Wang Y, Niu B, Su SF, Shi K. Event-triggered adaptive NN tracking control for nonlinear systems with asymmetric time-varying output constraints and application to AUVs. IEEE Trans. Veh. Technol. 2025;74(1):413–424.
38. Zhang G, Zhu Q. Event-triggered optimal control for nonlinear stochastic systems via adaptive dynamic programming. Nonlinear Dyn. 2021;105(1):387–401.
39. Wang Y, Zong G. Dynamic event-triggered adaptive fixed-time practical tracking control for nonlinear systems through funnel function. IEEE Trans. Autom. Sci. Eng. 2024;—(—):1–10.
40. Wang X, Wang T, Ding D. Barrier-function-enabled control for vessel systems under dynamic event-triggered protocols: The ADP approach. IEEE Trans. Intell. Vehicles 2024;—(—):1–10.
41. Li B, Chen N, Luo B, Chen J, Yang C, Gui W. ADP-based event-triggered constrained optimal control on spatiotemporal process: Application to temperature field in roller kiln. IEEE Trans. Neural Netw. Learn. Syst. 2024;35(3):3229–3241.
42. Wang J, Zhang Z, Tian B, Zong Q. Event-based robust optimal consensus control for nonlinear multiagent system with local adaptive dynamic programming. IEEE Trans. Neural Netw. Learn. Syst. 2024;35(1):1073–1086.
43. Fu H, He H, Chen Y. Event-triggered cooperative tracking control of multi-agent systems with a dynamic leader via approximate dynamic programming. IEEE Trans. Artif. Intell. 2024;5(6):2752–2765.
44. Ding K, Zhu Q. Fuzzy model-based quantitative control for prefixed time synchronization of stochastic reaction–diffusion complex networks under cyber-attacks. IEEE Trans. Autom. Sci. Eng. 2024;21(4):6693–6707.
45. Zong G, Xie H, Yang D, Zhao X, Yi Y. Adaptive fuzzy tracking control for switched nonlinear systems under FDI attacks and input saturation: A flexible transient performance approach. IEEE Trans. Cybern. 2024;54(12):7479–7488.
46. Wen G, Chen CLP, Ge SS, Yang H, Liu X. Optimized adaptive nonlinear tracking control using actor–critic reinforcement learning strategy. IEEE Trans. Ind. Informat. 2019;15(9):4969–4977.
47. Sun K, Sui S, Tong S. Fuzzy adaptive decentralized optimal control for strict-feedback nonlinear large-scale systems. IEEE Trans. Cybern. 2018;48(4):1326–1339.
48. Zhang H, Zhao X, Wang H, Zong G, Xu N. Hierarchical sliding mode surface-based adaptive actor–critic optimal control for switched nonlinear systems with unknown perturbation. IEEE Trans. Neural Netw. Learn. Syst. 2024;35(2):1559–1571.
49. Zhang G, Zhu Q. State and output feedback finite-time guaranteed cost H∞ control for uncertain nonlinear stochastic systems with time-varying delays. J. Franklin Inst. 2023;360(12):8037–8061.
50. Jia F, He X. Adaptive fault-tolerant tracking control for discrete-time non-strict-feedback nonlinear systems with stochastic noises. IEEE Trans. Autom. Sci. Eng. 2024;21(3):3344–3356.
51. Zhang G, Zhu Q. Finite-time guaranteed cost control for uncertain delayed switched nonlinear stochastic systems. J. Franklin Inst. 2022;359(16):8802–8818.
52. Li K, Li Y. Adaptive NN optimal consensus fault-tolerant control for stochastic nonlinear multiagent systems. IEEE Trans. Neural Netw. Learn. Syst. 2023;34(2):947–957.
2. B. Ning, Q.-L. Han, Z. Zuo, L. Ding, Q. Lu, and X. Ge, “Fixed-time and prescribed-time consensus control of multiagent systems and its applications: A survey of recent trends and methodologies,” IEEE Trans. Ind. Informat., vol. 19, no. 2, pp. 1121–1135, Feb. 2023.
3. J. Wang, J. Liu, Y. Li, C. L. P. Chen, Z. Liu, and F. Li, “Prescribed time fuzzy adaptive consensus control for multiagent systems with dead-zone input and sensor faults,” IEEE Trans. Autom. Sci. Eng., vol. 21, no. 3, pp. 4016–4027, Jul. 2024.
4. G. Wen and C. L. P. Chen, “Optimized backstepping consensus control using reinforcement learning for a class of nonlinear strict-feed back dynamic multi-agent systems,” IEEE Trans. Neural Netw. Learn. Syst., vol. 34, no. 3, pp. 1524–1536, Mar. 2023.
5. Q. Wei, X. Wang, X. Zhong, and N. Wu, “Consensus control of leader–following multi-agent systems in directed topology with heterogeneous disturbances,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 2, pp. 423–431, Feb. 2021.
6. M. Lv,W. Yu, J. Cao, and S. Baldi, “Consensus in high-power multiagent systems with mixed unknown control directions via hybrid Nussbaumbased control,” IEEE Trans. Cybern., vol. 52, no. 6, pp. 5184–5196, Jun. 2022.
7. D. Deng, P. He, P. Shi, and L. Kovács, “Distributed consensus of nonlinear stochastic multi-agent systems with input and output delays via predictive control,” Nonlinear Dyn., vol. 112, no. 23, pp. 21227–21239, Aug. 2024.
8. Y. Yang, Y. Pan, C. Z. Xu, and D. C. Wunsch, “Hamiltonian-driven adaptive dynamic programming with efficient experience replay,” IEEE Trans. Neural Netw. Learn. Syst., vol. 35, no. 3, pp. 3278–3290, Mar. 2024.
9. Z. Zhang, K. Zhang, X. Xie, and V. Stojanovic, “ADP-based prescribed time control for nonlinear time-varying delay systems with uncertain parameters,” IEEE Trans. Autom. Sci. Eng., vol. 22, pp. 3086–3096, Apr. 2024, doi: 10.1109/TASE.2024.3389020.
10. Q. Zhu, “Event-triggered sampling problem for exponential stability of stochastic nonlinear delay systems driven by Lévy processes,” IEEE Trans. Autom. Control, vol. 70, no. 2, pp. 1176–1183, Feb. 2025.
11. G. Zong, Y. Wang, B. Niu, S.-F. Su, and K. Shi, “Event-triggered adaptive NN tracking control for nonlinear systems with asymmetric time-varying output constraints and application to an AUVs,” IEEE Trans. Veh. Technol., vol. 74, no. 1, pp. 413–424, Jan. 2025.
12. G. Zhang and Q. Zhu, “Event-triggered optimal control for nonlinearstochastic systems via adaptive dynamic programming,” Nonlinear Dyn., vol. 105, no. 1, pp. 387–401, 2021.
13. Y. Wang and G. Zong, “Dynamic event-triggered adaptive fixedtime practical tracking control for nonlinear systems through funnel function,” IEEE Trans. Autom. Sci. Eng., pp. 1–10, Sep. 2024, doi: 10.1109/TASE.2024.3458176.
14. X. Wang, T. Wang, and D. Ding, “Barrier-function-enabled control for vessel systems under dynamic event-triggered protocols: The ADP approach,” IEEE Trans. Intell. Vehicles, pp. 1–10, Aug. 2024, doi: 10.1109/TIV.2024.3451517.
15. B. Li, N. Chen, B. Luo, J. Chen, C. Yang, and W. Gui, “ADP-based event-triggered constrained optimal control on spatiotemporal process: Application to temperature field in roller kiln,” IEEE Trans. Neural Netw. Learn. Syst., vol. 35, no. 3, pp. 3229–3241, Mar. 2024.
16. J. Wang, Z. Zhang, B. Tian, and Q. Zong, “Event-based robust optimalconsensus control for nonlinear multiagent system with local adaptive dynamic programming,” IEEE Trans. Neural Netw. Learn. Syst., vol. 35, no. 1, pp. 1073–1086, Jan. 2024.
17. H. Fu, H. He, and Y. Chen, “Event-triggered cooperative tracking control of multi agent systems with a dynamic leader via approximate dynamic programming,” IEEE Trans. Artif. Intell., vol. 5, no. 6, pp. 2752–2765, Jun. 2024.
18. K. Ding and Q. Zhu, “Fuzzy model-based quantitative control for prefixed time synchronization of stochastic reaction-diffusion complex networks under cyber-attacks,” IEEE Trans. Autom. Sci. Eng., vol. 21, no. 4, pp. 6693–6707, Oct. 2024.
19. G. Zong, H. Xie, D. Yang, X. Zhao, and Y. Yi, “Adaptive fuzzy tracking control for switched nonlinear systems under FDI attacks and input saturation: A flexible transient performance approach,” IEEE Trans. Cybern., vol. 54, no. 12, pp. 7479–7488, Dec. 2024.
20. G. Wen, C. L. P. Chen, S. S. Ge, H. Yang, and X. Liu, “Optimized adaptive nonlinear tracking control using actor–critic reinforcement learning strategy,” IEEE Trans. Ind. Informat., vol. 15, no. 9, pp. 4969–4977, Sep. 2019.
21. K. Sun, S. Sui, and S. Tong, “Fuzzy adaptive decentralized optimal control for strict feedback nonlinear large-scale systems,” IEEE Trans. Cybern., vol. 48, no. 4, pp. 1326–1339, Apr. 2018.
22. H. Zhang, X. Zhao, H. Wang, G. Zong, and N. Xu, “Hierarchical sliding mode surface-based adaptive actor–critic optimal control for switched nonlinear systems with unknown perturbation,” IEEE Trans. Neural Netw. Learn. Syst., vol. 35, no. 2, pp. 1559–1571, Feb. 2024.
23. G. Zhang and Q. Zhu, “State and output feedback’s finite-time guaranteed cost H∞ control for uncertain nonlinear stochastic systems with time-varying delays,” J. Franklin Inst., vol. 360, no. 12, pp. 8037–8061, Aug. 2023.
24. F. Jia and X. He, “Adaptive fault-tolerant tracking control for discrete time non strict-feedback nonlinear systems with stochastic noises,” IEEE Trans. Autom. Sci. Eng., vol. 21, no. 3, pp. 3344–3356, Jul. 2024.
25. G. Zhang and Q. Zhu, “Finite-time guaranteed cost control for uncertain delayed switched nonlinear stochastic systems,” J. Franklin Inst., vol. 359, no. 16, pp. 8802–8818, Nov. 2022.
26. K. Li and Y. Li, “Adaptive NN optimal consensus fault-tolerant control for stochastic nonlinear multiagent systems,” IEEE Trans. Neural Netw. Learn. Syst., vol. 34, no. 2, pp. 947–957, Feb. 2023.
27. Jiang Y, Niu B, Zong G, Zhao X, Zhao P. Distributed adaptive secure consensus tracking control for asynchronous switching nonlinear MASs under sensor attacks and actuator faults. IEEE Trans. Autom. Sci. Eng. 2024;21(3):3253–3263.
28. Ning B, Han QL, Zuo Z, Ding L, Lu Q, Ge X. Fixed-time and prescribed-time consensus control of multiagent systems and its applications: A survey of recent trends and methodologies. IEEE Trans. Ind. Informat. 2023;19(2):1121–1135.
29. Wang J, Liu J, Li Y, Chen CLP, Liu Z, Li F. Prescribed time fuzzy adaptive consensus control for multiagent systems with dead-zone input and sensor faults. IEEE Trans. Autom. Sci. Eng. 2024;21(3):4016–4027.
30. Wen G, Chen CLP. Optimized backstepping consensus control using reinforcement learning for a class of nonlinear strict-feedback dynamic multi-agent systems. IEEE Trans. Neural Netw. Learn. Syst. 2023;34(3):1524–1536.
31. Wei Q, Wang X, Zhong X, Wu N. Consensus control of leader–following multi-agent systems in directed topology with heterogeneous disturbances. IEEE/CAA J. Autom. Sinica 2021;8(2):423–431.
32. Lv M, Yu W, Cao J, Baldi S. Consensus in high-power multiagent systems with mixed unknown control directions via hybrid Nussbaum-based control. IEEE Trans. Cybern. 2022;52(6):5184–5196.
33. Deng D, He P, Shi P, Kovács L. Distributed consensus of nonlinear stochastic multi-agent systems with input and output delays via predictive control. Nonlinear Dyn. 2024;112(23):21227–21239.
34. Yang Y, Pan Y, Xu CZ, Wunsch DC. Hamiltonian-driven adaptive dynamic programming with efficient experience replay. IEEE Trans. Neural Netw. Learn. Syst. 2024;35(3):3278–3290.
35. Zhang Z, Zhang K, Xie X, Stojanovic V. ADP-based prescribed time control for nonlinear time-varying delay systems with uncertain parameters. IEEE Trans. Autom. Sci. Eng. 2024;22(—):3086–3096.
36. Zhu Q. Event-triggered sampling problem for exponential stability of stochastic nonlinear delay systems driven by Lévy processes. IEEE Trans. Autom. Control 2025;70(2):1176–1183.
37. Zong G, Wang Y, Niu B, Su SF, Shi K. Event-triggered adaptive NN tracking control for nonlinear systems with asymmetric time-varying output constraints and application to AUVs. IEEE Trans. Veh. Technol. 2025;74(1):413–424.
38. Zhang G, Zhu Q. Event-triggered optimal control for nonlinear stochastic systems via adaptive dynamic programming. Nonlinear Dyn. 2021;105(1):387–401.
39. Wang Y, Zong G. Dynamic event-triggered adaptive fixed-time practical tracking control for nonlinear systems through funnel function. IEEE Trans. Autom. Sci. Eng. 2024;—(—):1–10.
40. Wang X, Wang T, Ding D. Barrier-function-enabled control for vessel systems under dynamic event-triggered protocols: The ADP approach. IEEE Trans. Intell. Vehicles 2024;—(—):1–10.
41. Li B, Chen N, Luo B, Chen J, Yang C, Gui W. ADP-based event-triggered constrained optimal control on spatiotemporal process: Application to temperature field in roller kiln. IEEE Trans. Neural Netw. Learn. Syst. 2024;35(3):3229–3241.
42. Wang J, Zhang Z, Tian B, Zong Q. Event-based robust optimal consensus control for nonlinear multiagent system with local adaptive dynamic programming. IEEE Trans. Neural Netw. Learn. Syst. 2024;35(1):1073–1086.
43. Fu H, He H, Chen Y. Event-triggered cooperative tracking control of multi-agent systems with a dynamic leader via approximate dynamic programming. IEEE Trans. Artif. Intell. 2024;5(6):2752–2765.
44. Ding K, Zhu Q. Fuzzy model-based quantitative control for prefixed time synchronization of stochastic reaction–diffusion complex networks under cyber-attacks. IEEE Trans. Autom. Sci. Eng. 2024;21(4):6693–6707.
45. Zong G, Xie H, Yang D, Zhao X, Yi Y. Adaptive fuzzy tracking control for switched nonlinear systems under FDI attacks and input saturation: A flexible transient performance approach. IEEE Trans. Cybern. 2024;54(12):7479–7488.
46. Wen G, Chen CLP, Ge SS, Yang H, Liu X. Optimized adaptive nonlinear tracking control using actor–critic reinforcement learning strategy. IEEE Trans. Ind. Informat. 2019;15(9):4969–4977.
47. Sun K, Sui S, Tong S. Fuzzy adaptive decentralized optimal control for strict-feedback nonlinear large-scale systems. IEEE Trans. Cybern. 2018;48(4):1326–1339.
48. Zhang H, Zhao X, Wang H, Zong G, Xu N. Hierarchical sliding mode surface-based adaptive actor–critic optimal control for switched nonlinear systems with unknown perturbation. IEEE Trans. Neural Netw. Learn. Syst. 2024;35(2):1559–1571.
49. Zhang G, Zhu Q. State and output feedback finite-time guaranteed cost H∞ control for uncertain nonlinear stochastic systems with time-varying delays. J. Franklin Inst. 2023;360(12):8037–8061.
50. Jia F, He X. Adaptive fault-tolerant tracking control for discrete-time non-strict-feedback nonlinear systems with stochastic noises. IEEE Trans. Autom. Sci. Eng. 2024;21(3):3344–3356.
51. Zhang G, Zhu Q. Finite-time guaranteed cost control for uncertain delayed switched nonlinear stochastic systems. J. Franklin Inst. 2022;359(16):8802–8818.
52. Li K, Li Y. Adaptive NN optimal consensus fault-tolerant control for stochastic nonlinear multiagent systems. IEEE Trans. Neural Netw. Learn. Syst. 2023;34(2):947–957.
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