Volume 15, Issue 4 (Journal of Control, V.15, N.4 Winter 2022)
JoC 2022, 15(4): 25-37 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Karimi B, Khoshkhooie A. Adaptive Consensus Control for a Class of Non-affine MIMO Strict-Feedback Multi-Agent Systems with Time Delay. JoC 2022; 15 (4) :25-37
URL: http://joc.kntu.ac.ir/article-1-761-en.html
URL: http://joc.kntu.ac.ir/article-1-761-en.html
1- Malek Ashtar University
2- Malek Ashtar University
2- Malek Ashtar University
Abstract: (4711 Views)
In this paper, the design of a distributed adaptive controller for a class of unknown non-affine MIMO strict-feedback multi agent systems with time delay has been performed under a directed graph. The controller design is based on dynamic surface control method. In the design process, radial basis function neural networks (RBFNNs) were employed to approximate the unknown nonlinear functions. Stability analysis was performed using the Lyapunov-Krasovskii function and it was proved that all the signals of the closed-loop system are semi-globally uniformly bounded. Finally, the simulation results also confirmed the performance of the proposed control method.
Keywords: Adaptive neural control, Consensus, Dynamic surface control, Non-affine strict-feedback systems, Time delay
Type of Article: Research paper |
Subject:
Special
Received: 2020/05/15 | Accepted: 2021/04/25 | ePublished ahead of print: 2021/06/12 | Published: 2021/12/22
Received: 2020/05/15 | Accepted: 2021/04/25 | ePublished ahead of print: 2021/06/12 | Published: 2021/12/22
References
1. [1] Bauso D, Giarré L, Pesenti R. Non-linear protocols for optimal distributed consensus in networks of dynamic agents. Systems & Control Letters. 2006;55(11):918-28. [DOI:10.1016/j.sysconle.2006.06.005]
2. [2] Hou Z-G, Cheng L, Tan M. Decentralized robust adaptive control for the multiagent system consensus problem using neural networks. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics). 2009;39(3):636-47. [DOI:10.1109/TSMCB.2008.2007810]
3. [3] G.X. Wen CC, YJ. Liu. Neural-network-based adaptive leader-following consensus control for second-order non-linear multi-agent systems. IET Control Theory & Applications 2015; 9:1927-34. [DOI:10.1049/iet-cta.2014.1319]
4. [4] H.G. Sarand BK. Synchronisation of high-order MIMO nonlinear systems using distributed neuro-adaptive control. . International Journal of Systems Science. 2014; 47:2214-24. [DOI:10.1080/00207721.2014.980367]
5. [5] Lin Zhao JY, Haisheng Yu. distributed adaptive consensus tracking control for multiple AUVs. seventh international conference on information science and technology,Da Nang Vietnam. 2017. [DOI:10.1109/ICIST.2017.7926808]
6. [6] H.G. Sarand BK. adaptive neural network method for consensus tracking of high order mimo nonlinear multi agent systems. Amirkabir International Journal of Science & Research Modeling, Identification, Simulation & Control,. 2014;46 No. 2:11- 21.
7. [7] E. Nuno RO, L. Basanez, D. Hill. Synchronization of networks of nonidentical Euler-Lagrange systems with uncertain parameters and communication delays. IEEE Transactions on Automatic Control. 2011;56 935-41. [DOI:10.1109/TAC.2010.2103415]
8. [8] Abdessameud A, Polushin IG, Tayebi A. Synchronization of Lagrangian Systems With Irregular Communication Delays. IEEE Transactions on Automatic Control. 2014;59(1):187-93. [DOI:10.1109/TAC.2013.2270053]
9. [9] Min H, Sun F, Wang S, Li H. Distributed adaptive consensus algorithm for networked Euler-Lagrange systems. IET control theory & applications. 2011;5(1):145-54. [DOI:10.1049/iet-cta.2009.0607]
10. [10] Wen G-X, Chen CP, et al. Adaptive NN Consensus Control for a Class of Nonlinear Multi-agent Time-Delay Systems. Systems, Man, and Cybernetics (SMC), 2013 IEEE International Conference on; 2013: IEEE. [DOI:10.1109/SMC.2013.844]
11. [11] Chen, CL Philip, et al. Adaptive consensus control for a class of nonlinear multi-agent time-delay systems using neural networks. IEEE Transactions on Neural Networks and Learning Systems. 2014; 25(6):1217-1226. [DOI:10.1109/TNNLS.2014.2302477]
12. [12] Ma L, Min H, Wang S, Liu Y. Consensus of nonlinear multi-agent systems with self and communication time delays: A unified framework. Journal of the Franklin Institute. 2015;352(3):745-60. [DOI:10.1016/j.jfranklin.2014.05.010]
13. [13] Liu B, Wang X, Su H, Gao Y, Wang L. Adaptive second-order consensus of multi-agent systems with heterogeneous nonlinear dynamics and time-varying delays. Neurocomputing. 2013;118:289-300. [DOI:10.1016/j.neucom.2013.02.038]
14. [14] Jia Q, Tang WK, Halang WA. Leader following of nonlinear agents with switching connective network and coupling delay. IEEE Transactions on Circuits and Systems I: Regular Papers. 2011;58(10):2508-19. [DOI:10.1109/TCSI.2011.2131230]
15. [15] Wen G, Duan Z, Yu W, Chen G. Consensus of second-order multi-agent systems with delayed nonlinear dynamics and intermittent communications. International Journal of Control. 2013;86(2):322-31. [DOI:10.1080/00207179.2012.727473]
16. [16] Wang C, Zuo Z, Lin Z, Ding Z. Consensus control of a class of Lipschitz nonlinear systems with input delay. IEEE Transactions on Circuits and Systems I: Regular Papers. 2015;62(11):2730-8. [DOI:10.1109/TCSI.2015.2479046]
17. [17] Li H, Xu L, Xiao L, Lin L, editors. Adaptive second-order leader-following consensus of nonlinear multi-agent systems with time-varying delay. Control and Decision Conference (2014 CCDC), The 26th Chinese; 2014: IEEE. [DOI:10.1109/CCDC.2014.6852181]
18. [18] Yoo SJ. Distributed consensus tracking for multiple uncertain nonlinear strict-feedback systems under a directed graph. IEEE transactions on neural networks and learning systems. 2013;24(4):666-72. [DOI:10.1109/TNNLS.2013.2238554]
19. [19] Yoo SJ. Distributed adaptive containment control of uncertain nonlinear multi-agent systems in strict-feedback form. Automatica. 2013;49(7):2145-53. [DOI:10.1016/j.automatica.2013.03.007]
20. [20] Shen Q, Shi P. Distributed command filtered backstepping consensus tracking control of nonlinear multiple-agent systems in strict-feedback form. Automatica. 2015;53:120-4. [DOI:10.1016/j.automatica.2014.12.046]
21. [21] Zhang Y, Cui G, Zhuang G, Lu J, Li Z. Command filtered backstepping tracking control of uncertain nonlinear strict-feedback systems under a directed graph. Transactions of the Institute of Measurement and Control. 2017;39(7):1027-36. [DOI:10.1177/0142331216629198]
22. [22] Gang Wang, Chaoli Wang, Lin Li, Qinghui Du. Distributed adaptive consensus tracking control of higher-order nonlinear strict-feedback multi-agent systems using neural networks,Neurocomputing,2016 [DOI:10.1109/CCDC.2016.7531428]
23. ]23[ م. تاج و م. شهریاری " کنترل اجماع توزیع شده تطبیقی برای دسته¬ای از سیستم¬های چندعاملی غیرخطی نامعین و ناهمگون" مجله کنترل، جلد 13، شماره. 1، 1398.
24. [24] K. Chen, J. Wang, Y. Zhang, Z. Liu, Leader-following consensus for a class of nonlinear strick-feedback multiagent systems with state time-delays, IEEE Transactions on Systems, Man, and Cybernetics: Systems. (2018).doi: 101109/tsmc.2018.2813399. [DOI:10.1145/3194554]
25. [25] Krstic M, Kanellakopoulos I, Kokotovic PV. Nonlinear and adaptive control design: Wiley; 1995.
26. [26] Swaroop D, Hedrick JK, Yip PP, Gerdes JC. Dynamic surface control for a class of nonlinear systems. IEEE transactions on automatic control. 2000;45(10):1893-9. [DOI:10.1109/TAC.2000.880994]
27. [27] Yoo SJ. Distributed consensus tracking of a class of asynchronously switched nonlinear multi-agent systems. Automatica. 2018;87:421-7. [DOI:10.1016/j.automatica.2017.04.006]
28. [28] Yang Y, Yue D. Distributed adaptive consensus tracking for a class of multi-agent systems via output feedback approach under switching topologies. Neurocomputing. 2016;174:1125-32. [DOI:10.1016/j.neucom.2015.10.034]
29. [29] Wang W, Dan Wang, and Zhouhua Peng. Distributed containment control for uncertain nonlinear multi-agent systems in non-affine pure-feedback form under switching topologies. Neurocomputing 2015;152:1-10. [DOI:10.1016/j.neucom.2014.11.035]
30. [30] Shahvali M, and Khoshnam Shojaei. Distributed adaptive neural control of nonlinear multi-agent systems with unknown control directions. Nonlinear Dynamics 2016;83.4:2213-28. [DOI:10.1007/s11071-015-2476-4]
31. [31] Zhao Y, Chen G, editors. Distributed adaptive tracking control of non-affine nonlinear multi-agent systems. 2016 Chinese Control and Decision Conference (CCDC); 2016: IEEE. [DOI:10.1109/CCDC.2016.7531224]
32. [32] Lin W, Qian C. Adaptive control of nonlinearly parameterized systems: the smooth feedback case. IEEE Transactions on Automatic control. 2002;47(8):1249-66. [DOI:10.1109/TAC.2002.800773]
33. [33] Ge SS, Tee KP. Approximation-based control of nonlinear MIMO time-delay systems. Automatica. 2007;43(1):31-43. [DOI:10.1016/j.automatica.2006.08.003]
34. [34] Polycarpou MM, Mears MJ. Stable adaptive tracking of uncertain systems using nonlinearly parametrized on-line approximators. International journal of control. 1998;70(3):363-84. [DOI:10.1080/002071798222280]
35. [35] Slotine J-JE, Li W. Applied nonlinear control: Prentice hall Englewood Cliffs, NJ; 1991.
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
