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Tracking control for the nonlinear complex dynamical network assisted with outgoing links dynamics. (English) Zbl 1528.93097

Summary: Along with the numerous highly interconnected nodes in real networks, a complex dynamical network shows the dynamic behaviors not only in the nodes but also in the links between nodes. This implies that the complex dynamical network can be considered to be composed of the two interconnected subsystems, the nodes subsystem (NS) and links subsystem (LS), and thus it has become evident that the dynamics of LS plays an important role in assisting NS to emerge the dynamic behavior. However, all the dynamic links are rarely considered to be the dynamic system as a whole in the existing literature. In this article, the outgoing links vector is introduced to describe the state of LS and the dynamics of LS is proposed and utilized to investigate the tracking task of NS. Here, the bounded uncertainties are considered in the dynamic model of NS and the nonlinearities are considered in the dynamic model of LS, which is rarely considered in the existing literature, by which the adaptive controller is synthesized to overcome the uncertainties and complete the tracking task of NS assisted with the constructed coupling term in LS. The results in this article show that when the controlled NS is tracking the given target, the LS is tracking the proposed auxiliary target. Finally, the effectiveness of the proposed theoretic results is verified via the example.
{© 2022 John Wiley & Sons Ltd.}

MSC:

93C40 Adaptive control/observation systems
93C10 Nonlinear systems in control theory
93B70 Networked control
Full Text: DOI

References:

[1] HaleKR, ValdovinosFS, MartinezND. Mutualism increases diversity, stability and function of multiplex networks that integrate pollinators into food webs. Nature Commun. 2020;11(1):1‐14.
[2] ZhangDG, WuH, ZhaoPZ, et al. New approach of multi‐path reliable transmission for marginal wireless sensor network. Wirel Netw. 2020;26(2):1503‐1517.
[3] SunY, JiangC, WangZH, XiangLJ, ZhangH. Bi‐directional wireless power transfer for vehicle‐to‐grid systems. J Power Electron. 2018;18(4):1190‐1200.
[4] ColleranH. Market integration reduces kin density in women’s ego‐networks in rural Poland. Nature Commun. 2020;11(1):1‐9.
[5] BaggioG, BassettDS, PasqualettiF. Data‐driven control of complex networks. Nature Commun. 2021;12(1):1‐13.
[6] JiaT, BarabásiAL. Control capacity and a random sampling method in exploring controllability of complex networks. Sci Rep. 2013;3(1):1‐6.
[7] HerreraM, Pérez‐HernándezM, Kumar ParlikadA, IzquierdoJ. Multi‐agent systems and complex networks: review and applications in systems engineering. Processes. 2020;8(3):312.
[8] Pavón‐DomínguezP, Moreno‐PulidoS. A fixed‐mass multifractal approach for unweighted complex networks. Phys A Stat Mech Appl. 2020;541:123670.
[9] XingW, ShiP, AgarwalRK, LiLY. Robust
[( {H}_{\infty } \]\) pinning synchronization for complex networks with event‐triggered communication scheme. IEEE Trans Circuits Syst I Regul Pap. 2020;67(12):5233‐5245. · Zbl 1468.93020
[10] ZhangLL, WangYH, WangQY, QiaoSH, WangF. Generalized projective synchronization for networks with one crucial node and different dimensional nodes via a single controller. Asian J Control. 2020;22(4):1471‐1483. · Zbl 07872683
[11] ZhangLL, WangYH, WangQY, LeiYF, WangF. Matrix projective cluster synchronization for arbitrarily coupled networks with different dimensional nodes via nonlinear control. Int J Robust Nonlinear Control. 2019;29(11):3650‐3665. · Zbl 1426.93246
[12] ZhangLL, LeiYF, WangYH, ChenXS. Matrix projective synchronization for time‐varying disturbed networks with uncertain nonlinear structures and different dimensional nodes. Neurocomputing. 2018;311:11‐23.
[13] HuangYY, HuangLW, WangYH, PengYX, YuF. Shape synchronization in driver‐response of 4‐D chaotic system and its application in image encryption. IEEE Access. 2020;8:135308‐135319.
[14] WangYH, FanYQ, WangQY, ZhangY. Stabilization and synchronization of complex dynamical networks with different dynamics of nodes via decentralized controllers. IEEE Trans Circuits Syst I Regul Pap. 2012;59(8):1786‐1795. · Zbl 1468.93139
[15] LiZK, DuanZS, ChenGR, HuangL. Consensus of multiagent systems and synchronization of complex networks: a unified viewpoint. IEEE Trans Circuits Syst I Regul Pap. 2009;57(1):213‐224. · Zbl 1468.93137
[16] WangYH, WangWL, ZhangLL. State synchronization of controlled nodes via the dynamics of links for complex dynamical networks. Neurocomputing. 2020;384:225‐230.
[17] ZhaoP, WangYH. Asymptotical stability for complex dynamical networks via link dynamics. Math Methods Appl Sci. 2020;43(15):8706‐8713. · Zbl 1457.34094
[18] GaoZL, WangYH, ZhangLL. Adaptive control of structural balance for complex dynamical networks based on dynamic coupling of nodes. Int J Modern Phys B. 2018;32(4):1850042. · Zbl 1431.91304
[19] GaoZL, WangYH, ZhangLL, HuangYY, WangWL. The dynamic behaviors of nodes driving the structural balance for complex dynamical networks via adaptive decentralized control. Int J Modern Phys B. 2018;32(24):1850267. · Zbl 1423.93016
[20] GaoZL, WangYH. The structural balance analysis of complex dynamical networks based on nodes’ dynamical couplings. PLoS One. 2018;13(1):e0191941.
[21] GaoZL, WangYH, XiongJ, PanY, HuangYY. Structural balance control of complex dynamical networks based on state observer for dynamic connection relationships. Complexity. 2020;2020:5075487. · Zbl 1435.93013
[22] GaoZL, WangYH, PengY, LiuLZ, ChenHG. Adaptive control of the structural balance for a class of complex dynamical networks. J Syst Sci Complex. 2020;33(3):725‐742. · Zbl 1447.93167
[23] NianXH, FuXR, ChuXY, XiongHY, WangHB. Disturbance observer‐based distributed sliding mode control of multimotor web‐winding systems. IET Control Theory Appl. 2020;14(4):614‐625. · Zbl 07907132
[24] ChuXY, NianXH, XiongHY, WangHB. Robust fault estimation and fault tolerant control for three‐motor web‐winding systems. Int J Control. 2021;94(11):3009‐3021. · Zbl 1478.93134
[25] HouHL, NianXH, XuSZ. Decentralized guaranteed cost control with
([H {\kern0em }_{\infty } \]\) performance for large‐scale web‐winding system. Asian J Control. 2022;24(1):459‐473. · Zbl 07886983
[26] WeiCX, ShangXC. Analysis on nonlinear vibration of breathing cracked beam. J Sound Vib. 2019;461:114901.
[27] KimJS, XuYF, ZhuWD. Linear finite element modeling of joined structures with riveted connections. J Vib Acoust. 2020;142(2):021008.
[28] GaoPT, WangYH, LiuLZ, ZhangLL, TangX. Asymptotical state synchronization for the controlled directed complex dynamic network via links dynamics. Neurocomputing. 2021;448:60‐66.
[29] YuWW, ChenGR, LüJH. On pinning synchronization of complex dynamical networks. Automatica. 2009;45(2):429‐435. · Zbl 1158.93308
[30] FengYT, DuanZS, LvYZ, RenW. Some necessary and sufficient conditions for synchronization of second‐order interconnected networks. IEEE Trans Cybern. 2018;49(12):4379‐4387.
[31] WangJL, WeiPC, WuHN, HuangTW, XuM. Pinning synchronization of complex dynamical networks with multiweights. IEEE Trans Syst Man Cybern Syst. 2017;49(7):1357‐1370.
[32] PiXC, TangLK, ChenXZ. A directed weighted scale‐free network model with an adaptive evolution mechanism. Phys A Stat Mech Appl. 2021;572:125897. · Zbl 07458755
[33] ZinilliA, MarchiMD. Value‐added in high technology and industrial basic research: a weighted network observing the trade of high‐tech goods. Int J Comput Econ Econometr. 2020;10(4):398‐418.
[34] WangXM, RanYJ, JiaT. Measuring similarity in co‐occurrence data using ego‐networks. Chaos Interdiscipl J Nonlinear Sci. 2020;30(1):013101.
[35] LuoL, WangYH, FanYQ, ZhangY. Novel adaptive control design for nonlinear system with extended partition of unity method. Asian J Contr. 2013;15(3):911‐918. · Zbl 1327.93247
[36] HomaeinezhadMR, YaqubiS. Two‐sided linear matrix inequality solution of affine input matrix for feasible discrete finite‐time sliding mode control of uncertain nonlinear mechanical machines. J VibControl. 2020;26(23‐24):2243‐2260.
[37] WangCY, WangYH. Stability analysis of discretized structure systems based on the complex network with dynamics of time‐varying stiffness. Math Methods Appl Sci. 2021;44(17):13344‐13356. · Zbl 1478.93478
[38] HokansonJM, ConstantinePG. A Lipschitz matrix for parameter reduction in computational science. SIAM J Sci Comput. 2021;43(3):A1858‐A1880. · Zbl 1525.65061
[39] GudmundssonT, LaubAJ. Approximate solution of large sparse Lyapunov equations. IEEE Trans Automat Contr. 1994;39(5):1110‐1114. · Zbl 0816.93041
[40] BehrM, BennerP, HeilandJ. Solution formulas for differential Sylvester and Lyapunov equations. Calcolo. 2019;56(4):1‐33. · Zbl 1432.15015
[41] HuangN, SunZY, AndersonBD, DuanZS. Distributed and adaptive triggering control for networked agents with linear dynamics. Inf Sci. 2020;517:297‐314. · Zbl 1461.93309
[42] TaoG. A simple alternative to the Barbalat lemma. IEEE Trans Automat Contr. 1997;42(5):698. · Zbl 0881.93070
[43] SunMX. A Barbalat‐like lemma with its application to learning control. IEEE Trans Automat Contr. 2009;54(9):2222‐2225. · Zbl 1367.93445
[44] WangWD, ZhaoXQ. An epidemic model in a patchy environment. Math Biosci. 2004;190(1):97‐112. · Zbl 1048.92030
[45] LiuCG, LiuXP, WangHQ, GaoC, ZhouYC, LuSY. Event‐triggered adaptive finite‐time prescribed performance tracking control for uncertain nonlinear systems. Int J Robust Nonlinear Control. 2020;30(18):8449‐8468. · Zbl 1525.93249
[46] ZhaoYJ, LiuCG, LiuXP, WangHQ, ZhouYC. Adaptive tracking control for stochastic nonlinear systems with unknown virtual control coefficients. Int J Robust Nonlinear Control. 2022;32(3):1331‐1354. · Zbl 1527.93241
[47] WangHQ, KangSJ, ZhaoXD, XuN, LiTS. Command filter‐based adaptive neural control design for nonstrict‐feedback nonlinear systems with multiple actuator constraints. IEEE Trans Cybern. 2021. doi:10.1109/TCYB.2021.3079129
[48] BhatSP, BernsteinDS. Finite‐time stability of continuous autonomous systems. SIAM J Control Optim. 2000;38(3):751‐766. · Zbl 0945.34039
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