This paper studies the distributed output feedback consensus control for a class of uncertain nonlinear multi-agent systems (MASs). The dynamics of subsystems are heterogeneous and contain unknown control gains. A novel distributed fuzzy consensus control protocol is constructed by employing variable separation technology and adaptive backstepping technique. The main advantage of the protocol is that it is distributed only depending on the relative output information. Moreover, it does not require the observer information exchange, which can avoid network attacks. A numerical example is given to illustrate the effectiveness of the proposed scheme.
AminiAAsifAMohammadiA (2020) A unified optimization for resilient dynamic event-triggering consensus under denial of service. IEEE Transactions on Cybernetics52(5): 2872–2884.
2.
BocheHSchaeferRFPoorHV (2020) Denial-of-service attacks on communication systems: Detectability and jammer knowledge. IEEE Transactions on Signal Processing68: 3754–3768.
3.
ChoiYHYooSJ (2022) Neural-network-based distributed asynchronous event-triggered consensus tracking of a class of uncertain nonlinear multi-agent systems. IEEE Transactions on Neural Networks and Learning Systems33(7): 2965–2979.
4.
DingZT (2015) Adaptive consensus output regulation of a class of nonlinear systems with unknown high-frequency gain. Automatica51: 348–355.
5.
GaoRHuangJS (2020) Leader-following consensus of uncertain strict feedback multiagent systems subject to sensor and actuator attacks. International Journal of Robust and Nonlinear Control30(17): 7635–7654.
6.
GuoXYLiangHJPanYN (2020) Observer-based adaptive fuzzy tracking control for stochastic nonlinear multi-agent systems with dead-zone input. Applied Mathematics and Computation379: 125269.
7.
HouNWangZDHoDWC, et al. (2019) Robust partial-nodes-based state estimation for complex networks under deception attacks. IEEE Transactions on Cybernetics50(6): 2793–2802.
8.
HuaCCZhanLLGuanXP (2015) Distributed adaptive neural network output tracking of leader-following high-order stochastic nonlinear multiagent systems with unknown dead-zone input. IEEE Transactions on Cybernetics47(1): 177–185.
9.
LiYFParkJHWuLB, et al. (2022) Distributed output-feedback adaptive fuzzy leader-following consensus of stochastic nonlinear interconnected multiagent systems. IEEE Transactions on Systems, Man, and Cybernetics: Systems52(1): 311–323.
10.
LiYXYangGHTongSC (2018) Fuzzy adaptive distributed event-triggered consensus control of uncertain nonlinear multiagent systems. IEEE Transactions on Systems, Man, and Cybernetics: Systems49(9): 1777–1786.
11.
LiuYJMaLLiuL, et al. (2019) Adaptive neural network learning controller design for a class of nonlinear systems with time-varying state constraints. IEEE Transactions on Neural Networks and Learning Systems31(1): 66–75.
12.
LvYZWenGHHuangTW (2019) Adaptive protocol design for distributed tracking with relative output information: A distributed fixed-time observer approach. IEEE Transactions on Control of Network Systems7(1): 118–128.
13.
LvYZWenGHHuangTW, et al. (2020) Adaptive attack-free protocol for consensus tracking with pure relative output information. Automatica117: 108998.
14.
MarinoRTomeiP (1993) Global adaptive output-feedback control of nonlinear systems, part I: Linear parameterization. IEEE Transactions on Automatic Control38(1): 17–32.
15.
MengWCYangQMJagannathanS, et al. (2018) Distributed control of high-order nonlinear input constrained multiagent systems using a backstepping-free method. IEEE Transactions on Cybernetics49(11): 3923–3933.
16.
PeydayeshAArefiMM (2021) Neuro-adaptive distributed output-feedback containment control for multiagent systems with nonstrictfeedback nonlinear dynamics and input constraints. International Journal of Robust and Nonlinear Control31(7): 2465–2490.
17.
SahooPKRiedelT (1998) Mean Value Theorems And Functional Equations. Hackensack, NJ: World Scientific.
18.
Shahriari-KahkeshiMAfrushAPhamVT (2021) Adaptive consensus control of high-order uncertain nonlinear multi-agent systems with fuzzy dead-zone. International Journal of Fuzzy Systems23(3): 743–754.
19.
TaoYKYangFFHeP, et al. (2020) Distributed adaptive neural consensus control for stochastic nonlinear multiagent systems with whole state delays and multiple constraints. International Journal of Control, Automation and Systems18(9): 2398–2410.
20.
WangCRJiHB (2014) Leader-following consensus of multi-agent systems under directed communication topology via distributed adaptive nonlinear protocol. Systems & Control Letters70: 23–27.
21.
WangCRShengJJiHB (2014) Leader-following consensus of a class of nonlinear multi-agent systems via dynamic output feedback control. Transactions of the Institute of Measurement and Control37(2): 154–163.
22.
WangCRSunHZhangS (2022a) Distributed adaptive leader-following consensus control for a class of non-linear output feedback systems. IMA Journal of Mathematical Control and Information39(4): 1157–1172.
23.
WangCRZhangLCaoF (2023) Leader-following consensus control of second-order nonlinear multi-agent systems with applications to slew rate control. Asian Journal of Control25(3): 2286–2294.
24.
WangLX (1994) Adaptive Fuzzy Systems and Control: Design and Stability Analysis. Englewood Cliffs, NJ: Prentice-Hall.
25.
WangQFuJJWangJZ (2017) Fully distributed containment control of high-order multi-agent systems with nonlinear dynamics. Systems & Control Letters99: 33–39.
26.
WangWTongSC (2019) Observer-based adaptive fuzzy containment control for multiple uncertain nonlinear systems. IEEE Transactions on Fuzzy Systems27(11): 2079–2089.
27.
WangWLongJZhouJ, et al. (2021) Adaptive backstepping based consensus tracking of uncertain nonlinear systems with event-triggered communication. Automatica133: 109841.
28.
WuLBParkJHXieXP, et al. (2020) Distributed adaptive neural network consensus for a class of uncertain nonaffine nonlinear multi-agent systems. Nonlinear Dynamics100(2): 1243–1255.
29.
YangYMiaoSTYueD, et al. (2020) Adaptive neural containment seeking of stochastic nonlinear strict-feedback multi-agent systems. Neurocomputing400: 393–400.
30.
ZhangHWLewisFLDasA (2011) Optimal design for synchronization of cooperative systems: State feedback, observer and output feedback. IEEE Transactions on Automatic Control56(8): 1948–1952.
31.
ZhangQRLiuKXiaYQ, et al. (2019) Optimal stealthy deception attack against cyber-physical systems. IEEE Transactions on Cybernetics50(9): 3963–3972.
