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Hostile-based bipartite containment control of nonlinear fractional multi-agent systems with input delays: a signed graph approach under disturbance and switching networks

  • Received: 24 January 2024 Revised: 04 March 2024 Accepted: 08 March 2024 Published: 02 April 2024
  • MSC : 93A16, 93D50

  • This article addresses the hostile-based bipartite containment control of nonlinear fractional multi-agent systems (FMASs) with input delays. Several fundamental algebraic criteria have been offered by the use of signed graph theory. To make the controller design more realistic, we assumed that the controller was under some disturbance. For the analysis of bipartite containment control, we used a fixed and switching signed network. The commonly used Lyapunov function approach and the Razumikhin technique were used. The use of these techniques can conquer the challenge brought on by switching, temporal delays, and fractional mathematics. To better elucidate the theoretical results, two examples are provided.

    Citation: Asad Khan, Azmat Ullah Khan Niazi, Saadia Rehman, Sidra Ahmed. Hostile-based bipartite containment control of nonlinear fractional multi-agent systems with input delays: a signed graph approach under disturbance and switching networks[J]. AIMS Mathematics, 2024, 9(5): 12678-12699. doi: 10.3934/math.2024620

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  • This article addresses the hostile-based bipartite containment control of nonlinear fractional multi-agent systems (FMASs) with input delays. Several fundamental algebraic criteria have been offered by the use of signed graph theory. To make the controller design more realistic, we assumed that the controller was under some disturbance. For the analysis of bipartite containment control, we used a fixed and switching signed network. The commonly used Lyapunov function approach and the Razumikhin technique were used. The use of these techniques can conquer the challenge brought on by switching, temporal delays, and fractional mathematics. To better elucidate the theoretical results, two examples are provided.



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    [1] W. Ren, R. W. Beard, Distributed consensus in multi-vehicle cooperative control: theory and applications, London: Springer, 2008. https://doi.org/10.1007/978-1-84800-015-5
    [2] P. Ogren, E. Fiorelli, N. E. Leonard, Cooperative control of mobile sensor networks: adaptive gradient climbing in a distributed environment, IEEE Trans. Automat. Contr., 49 (2004), 1292–1302. https://doi.org/10.1109/TAC.2004.832203 doi: 10.1109/TAC.2004.832203
    [3] V. Gazi, K. M. Passino, Stability analysis of swarms, IEEE Trans. Automat. Contr., 48 (2003), 692–697. https://doi.org/10.1109/TAC.2003.809765 doi: 10.1109/TAC.2003.809765
    [4] R. Olfati-Saber, Flocking for multi-agent dynamic systems: algorithms and theory, IEEE Trans. Automat. Contr., 51 (2006), 401–420. https://doi.org/10.1109/TAC.2005.864190 doi: 10.1109/TAC.2005.864190
    [5] D. Meng, Y. Jia, Robust consensus algorithms for multiscale coordination control of multivehicle systems with disturbances, IEEE Trans. Ind. Electron., 63 (2015), 1107–1119. https://doi.org/10.1109/TIE.2015.2478740 doi: 10.1109/TIE.2015.2478740
    [6] C. Altafini, Dynamics ofpinion forming in structurally balanced social networks, PloS One, 7 (2012), e38135. https://doi.org/10.1371/journal.pone.0038135 doi: 10.1371/journal.pone.0038135
    [7] Z. Meng, G. Shi, K. H. Johansson, M. Cao, Y. Hong, Behaviors of networks with antagonistic interactions and switching topologies, Automatica, 73 (2016), 110–116. https://doi.org/10.1016/j.automatica.2016.06.022 doi: 10.1016/j.automatica.2016.06.022
    [8] S. Gao, G. Wen, X. Zhai, P. Zheng, Finite-/fixed-time bipartite consensus for first-order multi-agent systems via impulsive control, Appl. Math. Comput., 442 (2023), 127740. https://doi.org/10.1016/j.amc.2022.127740 doi: 10.1016/j.amc.2022.127740
    [9] Q. Wang, W. Zhong, J. Xu, W. He, D. Tan, Bipartite tracking consensus control of nonlinear high-order multi-agent systems subject to exogenous disturbances, IEEE Access, 7 (2019), 145910–145920. https://doi.org/10.1109/ACCESS.2019.2944759 doi: 10.1109/ACCESS.2019.2944759
    [10] Y. Xu, J. Wang, Y. Zhang, Y. Xu, Event-triggered bipartite consensus for high-order multi-agent systems with input saturation, Neurocomputing, 379 (2020), 284–295. https://doi.org/10.1016/j.neucom.2019.10.095 doi: 10.1016/j.neucom.2019.10.095
    [11] M. A. Haque, M. Egerstedt, C. F. Martin, First-order, networked control models of swarming silkworm moths, 2008 American Control Conference, IEEE, 2008. https://doi.org/10.1109/ACC.2008.4587085
    [12] S. Bayraktar, G. E. Fainekos, G. J. Pappas, Experimental cooperative control of fixed-wing unmanned aerial vehicles, 2004 43rd IEEE Conference on Decision and Control (CDC), IEEE, 2004. https://doi.org/10.1109/CDC.2004.1429426
    [13] I. Navarro, F. Matía, An introduction to swarm robotics, ISRN Robotics, 2013 (2013), 608164. https://doi.org/10.5402/2013/608164 doi: 10.5402/2013/608164
    [14] J. Hu, J. Yu, J. Cao, Distributed containment control for nonlinear multi‐agent systems with time‐delayed protocol, Asian J. Control, 18 (2016), 747–756. https://doi.org/10.1002/asjc.1131 doi: 10.1002/asjc.1131
    [15] H. Zhang, J. Chen, Bipartite consensus of multi‐agent systems over signed graphs: state feedback and output feedback control approaches, Int. J. Robust Nonlinear Control, 27 (2017), 3–14. https://doi.org/10.1002/rnc.3552 doi: 10.1002/rnc.3552
    [16] Q. Deng, J. Wu, T. Han, Q. S. Yang, X. S. Cai, Fixed-time bipartite consensus of multi-agent systems with disturbances, Phys. A: Stat. Mech. Appl., 516 (2019), 37–49. https://doi.org/10.1016/j.physa.2018.09.066 doi: 10.1016/j.physa.2018.09.066
    [17] H. Wang, W. Yu, G. Wen, G. Chen, Finite-time bipartite consensus for multi-agent systems on directed signed networks, IEEE Trans. Circuits Syst. I, 65 (2018), 4336–4348. https://doi.org/10.1109/TCSI.2018.2838087 doi: 10.1109/TCSI.2018.2838087
    [18] M. Ahsan, Q. Ma, Bipartite containment control of multi-agent systems, 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), IEEE, 2019. https://doi.org/10.1109/AIM.2019.8868456
    [19] L. Wang, T. Han, X. S. Zhan, J. Wu, H. Yan, Bipartite containment for linear multi‐agent systems subject to unknown exogenous disturbances, Asian J. Control, 24 (2022), 1836–1845. https://doi.org/10.1002/asjc.2580 doi: 10.1002/asjc.2580
    [20] S. Zuo, Y. Song, F. L. Lewis, A. Davoudi, Bipartite output containment of general linear heterogeneous multi‐agent systems on signed digraphs, IET Control Theory Appl., 12 (2018), 1180–1188. https://doi.org/10.1049/iet-cta.2017.0686 doi: 10.1049/iet-cta.2017.0686
    [21] Q. Zhou, W. Wang, H. Liang, M. V. Basin, B. Wang, Observer-based event-triggered fuzzy adaptive bipartite containment control of multiagent systems with input quantization, IEEE Trans. Fuzzy Syst., 29 (2019), 372–384. https://doi.org/10.1109/TFUZZ.2019.2953573 doi: 10.1109/TFUZZ.2019.2953573
    [22] L. Xing, C. Wen, Z. Liu, H. Su, J. Cai, Event-triggered output feedback control for a class of uncertain nonlinear systems, IEEE Trans. Automat. Contr., 64 (2018), 290–297. https://doi.org/10.1109/TAC.2018.2823386 doi: 10.1109/TAC.2018.2823386
    [23] Y. H. Choi, S. J. Yoo, Event-triggered output-feedback tracking of a class of nonlinear systems with unknown time delays, Nonlinear Dyn., 96 (2019), 959–973. https://doi.org/10.1007/s11071-019-04832-x doi: 10.1007/s11071-019-04832-x
    [24] X. Liu, Z. Zhang, H. Liu, Consensus control of fractional‐order systems based on delayed state fractional order derivative, Asian J. Control, 19 (2017), 2199–2210. https://doi.org/10.1002/asjc.1493 doi: 10.1002/asjc.1493
    [25] J. Liu, K. Qin, W. Chen, P. Li, M. Shi, Consensus of fractional-order multiagent systems with nonuniform time delays, Math. Probl. Eng., 2018 (2018), 2850757. https://doi.org/10.1155/2018/2850757 doi: 10.1155/2018/2850757
    [26] J. Shen, J. Cao, J. Lu, Consensus of fractional-order systems with non-uniform input and communication delays, Proc. Inst. Mech. Eng., Part I: J. Syst. Control Eng., 226 (2012), 271–283. https://doi.org/10.1177/0959651811412132 doi: 10.1177/0959651811412132
    [27] J. Shen, J. Cao, Necessary and sufficient conditions for consensus of delayed fractional‐order systems, Asian J. Control, 14 (2012), 1690–1697. https://doi.org/10.1002/asjc.492 doi: 10.1002/asjc.492
    [28] S. K. Panda, V. Vijayakumar, A. M. Nagy, Complex-valued neural networks with time delays in the $L^{p}$ sense: numerical simulations and finite time stability, Chaos Soliton. Fract., 177 (2023), 114263. https://doi.org/10.1016/j.chaos.2023.114263 doi: 10.1016/j.chaos.2023.114263
    [29] S. K. Panda, A. M. Nagy, V. Vijayakumar, B. Hazarika, Stability analysis for complex-valued neural networks with fractional order, Chaos Soliton. Fract., 175 (2023), 114045. https://doi.org/10.1016/j.chaos.2023.114045 doi: 10.1016/j.chaos.2023.114045
    [30] Y. Cao, A. Chandrasekar, T. Radhika, V. Vijayakumar, Input-to-state stability of stochastic Markovian jump genetic regulatory networks, Math. Comput. Simul., 2023, In press. https://doi.org/10.1016/j.matcom.2023.08.007
    [31] W. J. Lin, G. Tan, Q. G. Wang, J. Yu, Fault-tolerant state estimation for Markov jump neural networks with time-varying delays, IEEE Trans. Circuits Syst. II, 2023. https://doi.org/10.1109/TCSII.2023.3332390
    [32] J. Sun, J. Zhang, L. Liu, Y. Wu, Q. Shan, Output consensus control of multi-agent systems with switching networks and incomplete leader measurement, IEEE Trans. Automat. Sci. Eng., 2023, 1–10. https://doi.org/10.1109/TASE.2023.3328897
    [33] N. Sakthivel, Y. K. Ma, M. Mounika Devi, G. Manopriya, V. Vijayakumar, M. Huh, Nonuniform sampled-data control for synchronization of semi-Markovian jump stochastic complex dynamical networks with time-varying delays, Complexity, 2022 (2022), 2006947. https://doi.org/10.1155/2022/2006947 doi: 10.1155/2022/2006947
    [34] T. Radhika, A. Chandrasekar, V. Vijayakumar, Q. Zhu, Analysis of Markovian jump stochastic Cohen-Grossberg BAM neural networks with time delays for exponential input-to-state stability, Neural Process. Lett., 55 (2023), 11055–11072. https://doi.org/10.1007/s11063-023-11364-4 doi: 10.1007/s11063-023-11364-4
    [35] J. Hu, G. Tan, L. Liu, A new result on H$\infty$ state estimation for delayed neural networks based on an extended reciprocally convex inequality, IEEE Trans. Circuits Syst. II, 2023, 1181–1185. https://doi.org/10.1109/TCSII.2023.3323834 doi: 10.1109/TCSII.2023.3323834
    [36] J. Yuan, T. Chen, Observer‐based adaptive neural network dynamic surface bipartite containment control for switched fractional order multi‐agent systems, Int. J. Adapt. Control Signal Process., 36 (2022), 1619–1646. https://doi.org/10.1002/acs.3413 doi: 10.1002/acs.3413
    [37] M. I. Troparevsky, S. A. Seminara, M. A. Fabio, A review on fractional differential equations and a numerical method to solve some boundary value problems, In: W. Legnani, T. E. Moschandreou, Nonlinear systems: theoretical aspects and recent applications, IntechOpen, 2019, 3–22. https://doi.org/10.5772/intechopen.86273
    [38] S. Liu, R. Yang, X. F. Zhou, W. Jiang, X. Li, X. W. Zhao, Stability analysis of fractional delayed equations and its applications on consensus of multi-agent systems, Commun. Nonlinear Sci. Numer. Simul., 73 (2019), 351–362. https://doi.org/10.1016/j.cnsns.2019.02.019 doi: 10.1016/j.cnsns.2019.02.019
    [39] G. Shi, Y. Hong, K. H. Johansson, Connectivity and set tracking of multi-agent systems guided by multiple moving leaders, IEEE Trans. Automat. Contr., 57 (2011), 663–676. https://doi.org/10.1109/TAC.2011.2164733 doi: 10.1109/TAC.2011.2164733
    [40] J. Yu, X. Dong, Q. Li, Z. Ren, Practical time-varying formation tracking for second-order nonlinear multiagent systems with multiple leaders using adaptive neural networks, IEEE Trans. Neural Net. Learn. Syst., 29 (2018), 6015–6025. https://doi.org/10.1109/TNNLS.2018.2817880 doi: 10.1109/TNNLS.2018.2817880
    [41] J. Hu, P. Bhowmick, A. Lanzon, Distributed adaptive time-varying group formation tracking for multiagent systems with multiple leaders on directed graphs, IEEE Trans. Control Net. Syst., 7 (2019), 140–150. https://doi.org/10.1109/TCNS.2019.2913619 doi: 10.1109/TCNS.2019.2913619
    [42] S. Liu, R. Yang, X. Li, J. Xiao, Global attractiveness and consensus for Riemann-Liouville's nonlinear fractional systems with mixed time-delays, Chaos Soliton. Fract., 143 (2021), 110577. https://doi.org/10.1016/j.chaos.2020.110577 doi: 10.1016/j.chaos.2020.110577
    [43] R. Yang, S. Liu, Y. Y. Tan, Y. J. Zhang, W. Jiang, Consensus analysis of fractional-order nonlinear multi-agent systems with distributed and input delays, Neurocomputing, 329 (2019), 46–52. https://doi.org/10.1016/j.neucom.2018.10.045 doi: 10.1016/j.neucom.2018.10.045
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