The paper investigates the finite-time adaptive consensus control of nonlinear multi-agent systems (MASs) by output-feedback. During the process of control design, the fuzzy logic system (FLS) is employed to approximate nonlinear function, as well as constructing a fuzzy state observer to estimate unmeasured states. Combining the dynamic surface control (DSC) with adaptive backstepping method conquers the complexity explosion issue. Then, semi-global practical finite-time stability (SGPFS) for closed-loop system is obtained under arbitrary switching communication topologies. All the signals of the overall system are uniformly ultimately bounded (UUB). Finally, the availability of developed control approach will be demonstrated through a simulation.
ChaiXWangQDiaoQ, et al. (2022) Sampled-data-based dynamic event-triggered formation control for nonlinear multi-agent systems. Transactions of the Institute of Measurement and Control44(14): 2719–2728.
2.
ChenBWangFLiuXP, et al. (2018) Finite-time adaptive fuzzy tracking control design for nonlinear systems. IEEE Transactions on Fuzzy Systems26(3): 1209–1216.
3.
DongXLiYLuC, et al. (2018) Time-varying formation tracking for UAV swarm systems with switching directed topologies. IEEE Transactions on Neural Networks and Learning Systems30(12): 3674–3685.
4.
DongXWHuGQ (2016) Time-varying formation control for general linear multi-agent systems with switching directed topologies. Automatica73: 47–55.
5.
JiangJHJiangYY (2020) Leader-following consensus of linear time-varying multi-agent systems under fixed and switching topologies. Automatica113: 108804.
6.
KeighobadiJXuBAlfiA, et al. (2022) Compound FAT-based prespecified performance learning control of robotic manipulators with actuator dynamics. ISA Transactions131: 246–263.
7.
LiKLiY (2022) Fuzzy adaptive optimal consensus fault-tolerant control for stochastic nonlinear multiagent systems. IEEE Transactions on Fuzzy Systems30(8): 2870–2885.
8.
LiPDaiJYingJ, et al. (2020) Distributed adaptive fixed-time tracking consensus control for multiple uncertain nonlinear strict-feedback systems under a directed graph. Complexity2020: 1–21.
9.
LiYLiKTongS (2016) An observer-based fuzzy adaptive consensus control method for nonlinear multi-agent systems. IEEE Transactions on Fuzzy Systems.
10.
LiYLiKTongS (2022). An observer-based fuzzy adaptive consensus control method for nonlinear multiagent systems. IEEE Transactions on Fuzzy Systems, 30(11), 4667–4678.
11.
LiYQuFTongS (2021) Observer-based fuzzy adaptive finite-time containment control of nonlinear multiagent systems with input delay. IEEE Transactions on Cybernetics51(1): 126–137.
12.
LiuKXieGWangL (2014) Containment control for second-order multi-agent systems with time-varying delays. Systems & Control Letters67: 24–31.
13.
LiuSFengJWangQ, et al. (2022) Adaptive consensus control for a class of nonlinear multi-agent systems with unknown time delays and external disturbances. Transactions of the Institute of Measurement and Control44(10): 2063–2075.
14.
LuoXLiXLiX, et al. (2021) Bearing-only formation control of multi-agent systems in local reference frames. International Journal of Control94(5): 1261–1272.
15.
MeiJRenWChenJ (2015) Distributed consensus of second-order multi-agent systems with heterogeneous unknown inertias and control gains under a directed graph. IEEE Transactions on Automatic Control61(8): 2019–2034.
16.
Olfati-SaberRFaxJMurrayR (2007) Consensus and cooperation in networked multi-agent systems. Proceedings of the IEEE95(1): 215–233.
17.
ShiLShaoJZhengWX, et al. (2017) Asynchronous containment control for discrete-time second-order multi-agent systems with time-varying delays. Journal of the Franklin Institute354(18): 8552–8569.
18.
TangYGaoHZhangW, et al. (2013) Leader-following consensus of a class of stochastic delayed multi-agent systems with partial mixed impulses. Automatica53: 346–354.
19.
TongSSunKSuiS (2018) Observer-based adaptive fuzzy decentralized optimal control design for strict-feedback nonlinear large-scale systems. IEEE Transactions on Fuzzy Systems26(2): 569–584.
20.
TongSCLiYMFengG, et al. (2011) Observer-based adaptive fuzzy backstepping dynamic surface control for a class of MIMO nonlinear systems. IEEE Transactions on Systems, Man, and Cybernetics, Part B41(4): 1124–1135.
21.
WangCJiH (2015) Robust consensus tracking for a class of heterogeneous second-order nonlinear multi-agent systems. International Journal of Robust and Nonlinear Control25(17): 3367–3383.
22.
WangFChenBLinC, et al. (2017) Adaptive neural network finite-time output feedback control of quantized nonlinear systems. IEEE Transactions on Cybernetics48(6): 1839–1848.
23.
WangWPanYLiangH (2020) Adaptive neural finite-time containment control for nonlower triangular nonlinear multi-agent systems with dynamics uncertainties. Neurocomputing391: 157–166.
24.
WangXLiJXingJ, et al. (2018) A novel finite-time average consensus protocol for multi-agent systems with switching topology. Transactions of the Institute of Measurement and Control40(2): 606–614.
25.
WuWTongS (2022) Fixed-time adaptive fuzzy containment dynamic surface control for nonlinear multiagent systems. IEEE Transactions on Fuzzy Systems30(12): 5237–5248.
26.
WuWLiYTongS (2023) Neural network output-feedback consensus fault-tolerant control for nonlinear multiagent systems with intermittent actuator faults. IEEE Transactions on Neural Networks and Learning Systems34: 4728–4740.
27.
ZhanYSuiSTongS (2021) Adaptive fuzzy decentralized dynamic surface control for fractional-order nonlinear large-scale systems. IEEE Transactions on Fuzzy Systems30(8): 3373–3383.
28.
ZhangLChenBLinC, et al. (2021) Fuzzy adaptive finite-time consensus tracking control for nonlinear multi-agent systems. International Journal of Systems Science52(7): 1346–1358.
ZhangZLunzeJWangL (2020) Integral-based event-triggered control for multi-agent systems with general linear dynamics. International Journal of Control93(5): 1005–1014.
