Event-Triggered Asymmetric Bipartite Consensus Tracking for Nonlinear Multi-Agent Systems Based on Model-Free Adaptive Control

Jiaqi Liang; Xuhui Bu; Lizhi Cui; Zhongsheng Hou

doi:10.1109/JAS.2022.106070

Volume 10 Issue 3

Mar. 2023

IEEE/CAA Journal of Automatica Sinica

JCR Impact Factor: 15.3, Top 1 (SCI Q1)

CiteScore: 23.5, Top 2% (Q1)
Google Scholar h5-index: 77， TOP 5

Turn off MathJax

Article Contents

Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2023 > 10(3): 662-672

J. Q. Liang, X. H. Bu, L. Z. Cui, and Z. S. Hou, “Event-triggered asymmetric bipartite consensus tracking for nonlinear multi-agent systems based on model-free adaptive control,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 3, pp. 662–672, Mar. 2023. doi: 10.1109/JAS.2022.106070

Citation:

J. Q. Liang, X. H. Bu, L. Z. Cui, and Z. S. Hou, “Event-triggered asymmetric bipartite consensus tracking for nonlinear multi-agent systems based on model-free adaptive control,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 3, pp. 662–672, Mar. 2023. doi: 10.1109/JAS.2022.106070

Citation:

J. Q. Liang, X. H. Bu, L. Z. Cui, and Z. S. Hou, “Event-triggered asymmetric bipartite consensus tracking for nonlinear multi-agent systems based on model-free adaptive control,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 3, pp. 662–672, Mar. 2023. doi: 10.1109/JAS.2022.106070

PDF( 1283 KB)

Event-Triggered Asymmetric Bipartite Consensus Tracking for Nonlinear Multi-Agent Systems Based on Model-Free Adaptive Control

doi: 10.1109/JAS.2022.106070

Jiaqi Liang^{1
,
,},
Xuhui Bu^1
,,
Lizhi Cui^1
,,
Zhongsheng Hou^2
,

1.
School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454003, China
2.
School of Automation, Qingdao University, Qingdao 266071, China

Funds: This work was supported in part by the National Natural Science Foundation of China (U1804147, 61833001, 61873139, 61573129), the Innovative Scientists and Technicians Team of Henan Polytechnic University (T2019-2), and the Innovative Scientists and Technicians Team of Henan Provincial High Education (20IRTSTHN019)

More Information

Author Bio:
Jiaqi Liang received the bachelor degree in computer science and technology from Henan University of Science and Technology, in 2010, and the master degree and the Ph.D. degree in control science and engineering and mineral engineering from Henan Polytechnic University, in 2018 and 2022, respectively. She is currently a Postdoctoral at Henan Polytechnic University. Her research interests include data-driven control, iterative learning control, and multi-agent systems

Xuhui Bu (Member, IEEE) received the bachelor and master degrees in automation control from Henan Polytechnic University, in 2004 and 2007, respectively, and the Ph.D. degree in control theory and application from Beijing Jiaotong University, in 2011. He is currently a Full Professor in Henan Polytechnic University. He has authored over 60 peer-reviewed journal papers and over 20 papers in prestigious conference proceedings. His research interests include data-driven control, iterative learning control, traffic control, and networked system control

Lizhi Cui received the B.S. and M.S. degrees in automation control from Henan Polytechnic University, in 2003 and 2006, respectively, and the Ph.D. degree in control science and engineering from East China University of Science and Technology, in 2015. He is currently a Full Associate Professor with Henan Polytechnic University. He has authored over 20 peer-reviewed journal articles and over 10 articles in prestigious conference proceedings. His research interests include artificial intelligent signal processing, control engineering and application, and networked system control

Zhongsheng Hou (Fellow, IEEE) received the bachelor and master degrees both in applied mathematics from Jilin University of Technology, in 1983 and 1988, respectively, and the Ph.D. degree in control theory and applications from Northeastern University, in 1994. From 1997 to 2018, he was with Beijing Jiaotong University, where he was a Distinguished Professor and the Head of the Department of Automatic Control. He is currently the Chair Professor with the School of Automation, Qingdao University. He has published over 200 journal paper and has authored a monograph Model-Free Adaptive Control: Theory and Applications (CRC Press, 2013). His research interests include data-driven control, model-free adaptive control, learning control, and intelligent transportation systems
Corresponding author: Jiaqi Liang, e-mail: liang_jq@163.com
Received Date: 2022-07-10
Accepted Date: 2022-08-20

Available Online: 2022-11-18

Abstract

Abstract

In this paper, an asymmetric bipartite consensus problem for the nonlinear multi-agent systems with cooperative and antagonistic interactions is studied under the event-triggered mechanism. For the agents described by a structurally balanced signed digraph, the asymmetric bipartite consensus objective is firstly defined, assigning the agents’ output to different signs and module values. Considering with the completely unknown dynamics of the agents, a novel event-triggered model-free adaptive bipartite control protocol is designed based on the agents’ triggered outputs and an equivalent compact form data model. By utilizing the Lyapunov analysis method, the threshold of the triggering condition is obtained. Subsequently, the asymptotic convergence of the tracking error is deduced and a sufficient condition is obtained based on the contraction mapping principle. Finally, the simulation example further demonstrates the effectiveness of the protocol.
- Asymmetric bipartite,
- consensus tracking,
- event-triggered,
- model-free adaptive control (MFAC),
- nonlinear systems,
- signed digraph

FullText(HTML)

References(43)

References

[1]	R. Olfati-Saber and R. M. Murray, “Consensus problems in networks of agents with switching topology and time-delays,” IEEE Trans. Autom. Control, vol. 49, no. 9, pp. 1520–1533, Sept. 2004. doi: 10.1109/TAC.2004.834113
[2]	W. Ren, R. W. Beard, and E. M. Atkins, “Information consensus in multivehicle cooperative control,” IEEE Control Syst. Mag., vol. 27, no. 2, pp. 71–82, Apr. 2007. doi: 10.1109/MCS.2007.338264
[3]	Y. C. Cao, W. W. Yu, W. Ren, and G. R. Chen, “An overview of recent progress in the study of distributed multi-agent coordination,” IEEE Trans. Ind. Inf., vol. 9, no. 1, pp. 427–438, Feb. 2013. doi: 10.1109/TII.2012.2219061
[4]	B. D. Ning, Q. L. Han, and L. Ding, “Distributed finite-time secondary frequency and voltage control for islanded microgrids with communication delays and switching topologies,” IEEE Trans. Cybern., vol. 51, no. 8, pp. 3988–3999, Aug. 2021. doi: 10.1109/TCYB.2020.3003690
[5]	X. H. Ge, S. Y. Xiao, Q. L. Han, X. M. Zhang, and D. R. Ding, “Dynamic event-triggered scheduling and platooning control co-design for automated vehicles over vehicular ad-hoc networks,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 1, pp. 31–46, Jan. 2022. doi: 10.1109/JAS.2021.1004060
[6]	H. F. Song, D. R. Ding, H. L. Dong, and X. J. Yi, “Distributed filtering based on Cauchy-kernel-based maximum correntropy subject to randomly occurring cyber-attacks,” Automatica, vol. 135, p. 110004, Jan. 2022. doi: 10.1016/j.automatica.2021.110004
[7]	X. M. Zhang, Q. L. Han, X. H. Ge, D. R. Ding, L. Ding, D. Yue, and C. Peng, “Networked control systems: A survey of trends and techniques,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 1, pp. 1–17, Jan. 2020. doi: 10.1109/JAS.2019.1911861
[8]	H. Yu and X. H. Xia, “Adaptive consensus of multi-agents in networks with jointly connected topologies,” Automatica, vol. 48, no. 8, pp. 1783–1790, Aug. 2012. doi: 10.1016/j.automatica.2012.05.068
[9]	B. D. Ning, Q. L. Han, Z. Y. Zuo, J. Jin, and J. C. Zheng, “Collective behaviors of mobile robots beyond the nearest neighbor rules with switching topology,” IEEE Trans. Cybern., vol. 48, no. 5, pp. 1577–1590, May 2018. doi: 10.1109/TCYB.2017.2708321
[10]	C. Altafini, “Consensus problems on networks with antagonistic interactions,” IEEE Trans. Autom. Control, vol. 58, no. 4, pp. 935–946, Apr. 2013. doi: 10.1109/TAC.2012.2224251
[11]	S. X. Miao and H. S. Su, “Bipartite consensus for second-order multiagent systems with matrix-weighted signed network,” IEEE Trans. Cybern., vol. 52, no. 12, pp. 13038–13047, Dec. 2022.
[12]	D. Y. Meng, M. J. Du, and Y. M. Jia, “Interval bipartite consensus of networked agents associated with signed digraphs,” IEEE Trans. Autom. Control, vol. 61, no. 12, pp. 3755–3770, Dec. 2016. doi: 10.1109/TAC.2016.2528539
[13]	M. E. Valcher and Misra, “On the consensus and bipartite consensus in high-order multi-agent dynamical systems with antagonistic interactions,” Syst. Control Lett., vol. 66, pp. 94–103, Apr. 2014. doi: 10.1016/j.sysconle.2014.01.006
[14]	P. Gong and Q.-L. Han, “Fixed-time bipartite consensus tracking of fractional-order multi-agent systems with a dynamic leader,” IEEE Trans. Circuits Syst. II: Express Briefs, vol. 67, no. 10, pp. 2054–2058, Oct. 2020. doi: 10.1109/TCSII.2019.2947353
[15]	H. Wang, W. W. Yu, G. H. Wen, and G. R. Chen, “Finite-time bipartite consensus for multi-agent systems on directed signed networks,” IEEE Trans. Circuits Syst. I: Regular Papers, vol. 65, no. 12, pp. 4336–4348, Dec. 2018. doi: 10.1109/TCSI.2018.2838087
[16]	L. L. Chen, L. Shi, Q. Zhou, H. M. Sheng, and Y. H. Cheng, “Secure bipartite tracking control for linear leader-following multiagent systems under denial-of-service attacks,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 8, pp. 1512–1515, Aug. 2022. doi: 10.1109/JAS.2022.105758
[17]	U. Tumerdem, T. Shimono, and K. Ohnishi, “Asymmetric multilateral teleoperation through scaled consensus reaching on graphs,” IEEJ Trans. Ind. Appl., vol. 129, no. 10, pp. 972–980, Oct. 2009. doi: 10.1541/ieejias.129.972
[18]	M. Shahbazi, S. F. Atashzar, and R. V. Patel, “A systematic review of multilateral teleoperation systems,” IEEE Trans. Haptics, vol. 11, no. 3, pp. 338–356, Jul.–Sept. 2018. doi: 10.1109/TOH.2018.2818134
[19]	S. Hussain, S. Q. Xie, and K. Jamwal, “Adaptive impedance control of a robotic orthosis for gait rehabilitation,” IEEE Trans. Cybern., vol. 43, no. 3, pp. 1025–1034, Jun. 2013. doi: 10.1109/TSMCB.2012.2222374
[20]	E. T. Wolbrecht, V. Chan, D. J. Reinkensmeyer, and J. E. Bobrow, “Optimizing compliant, model-based robotic assistance to promote neurorehabilitation,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 16, no. 3, pp. 286–297, Jun. 2008. doi: 10.1109/TNSRE.2008.918389
[21]	D. V. Dimarogonas, E. Frazzoli, and K. H. Johansson, “Distributed event-triggered control for multi-agent systems,” IEEE Trans. Autom. Control, vol. 57, no. 5, pp. 1291–1297, May 2012. doi: 10.1109/TAC.2011.2174666
[22]	C. Nowzari and J. Cortés, “Distributed event-triggered coordination for average consensus on weight-balanced digraphs,” Automatica, vol. 68, pp. 237–244, Jun. 2016. doi: 10.1016/j.automatica.2016.01.069
[23]	Y. Fan, G. Feng, Y. Wang, and C. Song, “Distributed event-triggered control of multi-agent systems with combinational measurements,” Automatica, vol. 49, no. 2, pp. 671–675, Feb. 2013. doi: 10.1016/j.automatica.2012.11.010
[24]	L. T. Xing, C. Y. Wen, F. H. Guo, Z. T. Liu, and H. Y. Su, “Event-based consensus for linear multiagent systems without continuous communication,” IEEE Trans. Cybern., vol. 47, no. 8, pp. 2132–2142, Aug. 2017. doi: 10.1109/TCYB.2016.2610419
[25]	G. Guo, L. Ding, and Q.-L. Han, “A distributed event-triggered transmission strategy for sampled-data consensus of multi-agent systems,” Automatica, vol. 50, no. 5, pp. 1489–1496, May 2014. doi: 10.1016/j.automatica.2014.03.017
[26]	X. Chen, H. Yu, and F. Hao, “Prescribed-time event-triggered bipartite consensus of multiagent systems,” IEEE Trans. Cybern., vol. 52, no. 4, pp. 2589–2598, Apr. 2022. doi: 10.1109/TCYB.2020.3004572
[27]	A. H. Hu, Y. Y. Wang, J. D. Cao, and A. Alsaedi, “Event-triggered bipartite consensus of multi-agent systems with switching partial couplings and topologies,” Inf. Sci., vol. 521, pp. 1–13, Jun. 2020. doi: 10.1016/j.ins.2020.02.038
[28]	H. Yu, X. Chen, T. W. Chen, and F. Hao, “Event-triggered bipartite consensus for multiagent systems: A zeno-free analysis,” IEEE Trans. Autom. Control, vol. 65, no. 11, pp. 4866–4873, Nov. 2020. doi: 10.1109/TAC.2019.2962092
[29]	J. Ren, Q. Song, and G. Lu, “Event-triggered bipartite leader-following consensus of second-order nonlinear multi-agent systems under signed digraph,” J. Franklin Inst., vol. 356, no. 12, pp. 6591–6609, Aug. 2019. doi: 10.1016/j.jfranklin.2019.06.034
[30]	Y. L. Cai, H. G. Zhang, W. H. Li, Y. F. Mu, and Q. He, “Distributed bipartite adaptive event-triggered fault-tolerant consensus tracking for linear multiagent systems under actuator faults,” IEEE Trans. Cybern., vol. 52, no. 11, pp. 11313–11324, Nov. 2022. doi: 10.1109/TCYB.2021.3069955
[31]	X. L. Wang, D. R. Ding, X. H. Ge, and Q.-L. Han, “Neural-network-based control for discrete-time nonlinear systems with denial-of-service attack: The adaptive event-triggered case,” Int. J. Robust Nonlinear Control, vol. 32, no. 5, pp. 2760–2779, Mar. 2022. doi: 10.1002/rnc.5831
[32]	W. Zhao, Y. J. Liu, and L. Liu, “Observer-based adaptive fuzzy tracking control using integral barrier Lyapunov functionals for a nonlinear system with full state constraints,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 3, pp. 617–627, Mar. 2021. doi: 10.1109/JAS.2021.1003877
[33]	T. T. Gao, Y. J. Liu, L. Liu, and D. Li, “Adaptive neural network-based control for a class of nonlinear pure-feedback systems with time-varying full state constraints,” IEEE/CAA J. Autom. Sinica, vol. 5, no. 5, pp. 923–933, Sept. 2018. doi: 10.1109/JAS.2018.7511195
[34]	H. G. Zhang, H. Jiang, Y. H. Luo, and G. Y. Xiao, “Data-driven optimal consensus control for discrete-time multi-agent systems with unknown dynamics using reinforcement learning method,” IEEE Trans. Ind. Electron., vol. 64, no. 5, pp. 4091–4100, May 2017. doi: 10.1109/TIE.2016.2542134
[35]	D. Y. Meng and J. Y. Zhang, “Robust optimization-based iterative learning control for nonlinear systems with nonrepetitive uncertainties,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 5, pp. 1001–1014, May 2021. doi: 10.1109/JAS.2021.1003973
[36]	Z. S. Hou and S. T. Jin, “A novel data-driven control approach for a class of discrete-time nonlinear systems,” IEEE Trans. Control Syst. Technol., vol. 19, no. 6, pp. 1549–1558, Nov. 2011. doi: 10.1109/TCST.2010.2093136
[37]	X. H. Bu, Z. S. Hou, and H. W. Zhang, “Data-driven multiagent systems consensus tracking using model free adaptive control,” IEEE Trans. Neural Netw. Learn. Syst., vol. 29, no. 5, pp. 1514–1524, May 2018. doi: 10.1109/TNNLS.2017.2673020
[38]	Y. C. Wang, X. J. Qiu, H. G. Zhang, and X. Xie, “Data-driven-based event-triggered control for nonlinear CPSs against jamming attacks,” IEEE Trans. Neural Netw. Learn. Syst., vol. 33, no. 7, pp. 3171–3177, Jul. 2022. doi: 10.1109/TNNLS.2020.3047931
[39]	D. Liu and G. H. Yang, “Neural network-based event-triggered MFAC for nonlinear discrete-time processes,” Neurocomputing, vol. 272, pp. 356–364, Jan. 2018. doi: 10.1016/j.neucom.2017.07.008
[40]	X. H. Bu, W. Yu, Q. X. Yu, Z. S. Hou, and J. Q. Yang, “Event-triggered model-free adaptive iterative learning control for a class of nonlinear systems over fading channels,” IEEE Trans. Cybern., vol. 52, no. 9, pp. 9597–9608, Sept. 2022. doi: 10.1109/TCYB.2021.3058997
[41]	N. Lin, R. H. Chi, and B. Huang, “Event-triggered learning consensus of networked heterogeneous nonlinear agents with switching topologies,” J. Franklin Inst., vol. 358, no. 7, pp. 3803–3821, May 2021. doi: 10.1016/j.jfranklin.2021.02.025
[42]	X. Guo, J. L. Liang, and J. Q. Lu, “Asymmetric bipartite consensus over directed networks with antagonistic interactions,” IET Control Theory Appl., vol. 12, no. 17, pp. 2295–2301, Nov. 2018. doi: 10.1049/iet-cta.2018.5060
[43]	J. Q. Liang, X. H. Bu, L. Z. Cui, and Z. S. Hou, “Finite time asymmetric bipartite consensus for multi-agent systems based on iterative learning control,” Int. J. Rqobust Nonlinear Control, vol. 31, no. 12, pp. 5708–5724, Aug. 2021. doi: 10.1002/rnc.5568

Supplements(0)

Cited By

Proportional views

Proportional views

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Figures(6) / Tables(1)

Get Citation

PDF

XML

Article Metrics

Article views (526) PDF downloads(115)

Highlights

An asymmetric bipartite consensus control for the multi-agent systems is studied, where it aims to drive the agents at an agreement that different modulus and signs under the cooperative and antagonistic interactions
An event-triggered MFAC data-driven protocol is proposed for the completely unknown nonlinear multi-agent systems in an asynchronous triggering way
An asymmetric coefficient related sufficient condition and a requirement on signed digraph that out-degree not lager than in-degree are derived for the system convergence

Event-Triggered Asymmetric Bipartite Consensus Tracking for Nonlinear Multi-Agent Systems Based on Model-Free Adaptive Control

doi: 10.1109/JAS.2022.106070

Abstract

References

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Highlights

Export File

Citation

Format

Content