Consensus Control of Leader-Following Multi-Agent Systems in Directed Topology With Heterogeneous Disturbances

Qinglai Wei; Xin Wang; Xiangnan Zhong; Naiqi Wu

doi:10.1109/JAS.2021.1003838

Volume 8 Issue 2

Feb. 2021

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 > 2021 > 8(2): 423-431

Qinglai Wei, Xin Wang, Xiangnan Zhong and Naiqi Wu, "Consensus Control of Leader-Following Multi-Agent Systems in Directed Topology With Heterogeneous Disturbances," IEEE/CAA J. Autom. Sinica, vol. 8, no. 2, pp. 423-431, Feb. 2021. doi: 10.1109/JAS.2021.1003838

Citation:

Qinglai Wei, Xin Wang, Xiangnan Zhong and Naiqi Wu, "Consensus Control of Leader-Following Multi-Agent Systems in Directed Topology With Heterogeneous Disturbances," IEEE/CAA J. Autom. Sinica, vol. 8, no. 2, pp. 423-431, Feb. 2021. doi: 10.1109/JAS.2021.1003838

Citation:

PDF( 4014 KB)

Consensus Control of Leader-Following Multi-Agent Systems in Directed Topology With Heterogeneous Disturbances

doi: 10.1109/JAS.2021.1003838

Funds: This work was supported in part by the National Natural Science Foundation of China (61722312, 61533017, 62073321) and the National Key Research and Development Program of China (2018YFB1702300)

More Information

Author Bio:
Qinglai Wei (M’11–SM’20) received the B.S. degree in automation, and the Ph.D. degree in control theory and control engineering, from Northeastern University, in 2002 and 2009, respectively. From 2009 to 2011, he was a Postdoctoral Fellow with the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences. He is currently a Professor of the institute and the Associate Director of the laboratory. He has authored four books, and published over 80 international journal papers. His research interests include adaptive dynamic programming, neural-networks-based control, optimal control, nonlinear systems and their industrial applications.He is the Secretary of IEEE Computational Intelligence Society (CIS) Beijing Chapter since 2015. He was guest editors for several international journals. He was a recipient of IEEE/CAA Journal of Automatica Sinica Best Paper Award, IEEE System, Man, and Cybernetics Society Andrew P. Sage Best Transactions Paper Award, IEEE Transactions on Neural Networks and Learning Systems Outstanding Paper Award, the Outstanding Paper Award of Acta Automatica Sinica, IEEE the 6th Data Driven Control and Learning Systems Conference (DDCLS2017) Best Paper Award, and Zhang Siying Outstanding Paper Award of Chinese Control and Decision Conference (CCDC). He was a recipient of Shuang-Chuang Talents in Jiangsu Province, China, Young Researcher Award of Asia Pacific Neural Network Society (APNNS), Young Scientst Award and Yang Jiachi Tech Award of Chinese Association of Automation (CAA). He is a Board of Governors (BOG) member of the International Neural Network Society (INNS) and a council member of CAA

Xin Wang received the B.S. degree in electronic information engineering from Zhengzhou University, in 2012, and the M.S. degree in control engineering from University of Science and Technology Beijing, in 2015.He is currently pursuing the Ph.D. degree in control theory and control engineering with the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, and the University of Chinese Academy of Sciences, Beijing. His current research interests include reinforcement learning, adaptive dynamic programming, optimal control, and multi-agent system

Xiangnan Zhong (M’17) received the B.S. and M.S. degrees in automation, and control theory and control engineering from Northeastern University, in 2010 and 2012, respectively and the Ph.D. degree with the Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI, USA, in 2017.She is currently an Assistant Professor with the Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, USA. Her current research interests include computational intelligence, reinforcement learning, cyber-physical systems, networked control systems, neural network, and optimal control. Dr. Zhong was a recipient of the International Neural Network Society Doctoral Dissertation Award in 2019. She has been actively involved in numerous conference and workshop organization committees in the society

Naiqi Wu (M’04–SM’05–F’19) received the B.S. degree in electrical engineering from Anhui University of Technology, Huainan, China, in 1982, the M.S. and Ph.D. degrees in systems engineering both from Xi’an Jiaotong University, Xi’an, China, in 1985 and 1988, respectively. From 1988 to 1995, he was with Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China and from 1995 to 1998, with Shantou University, Shantou, China. He moved to Guangdong University of Technology, Guangzhou, China in 1998. He joined Macau University of Science and Technology, Macau, China in 2013. He was a Visiting Professor at Arizona State University, Tempe, USA in 1999; New Jersey Institute of Technology, Newark, USA, in 2004; University of Technology of Troyes, Troyes, France, from 2007 to 2009; and Evry University, Evry, France, from 2010 to 2011.He is currently a Chair Professor at the Institute of Systems Engineering, Macau University of Science and Technology, Macau, China. His research interests include production planning and scheduling, manufacturing system modeling and control, discrete event systems, Petri net theory and applications, intelligent transportation systems, and energy systems. He is the author or coauthor of one book, five book chapters, and 170+ peer-reviewed journal papers. Dr. Wu was an Associate Editor of the IEEE Transactions on Systems, Man, and Cybernetics, Part C, IEEE Transactions on Automation Science and Engineering, IEEE Transactions on Systems, Man, and Cybernetics: Systems, and Editor in Chief of Industrial Engineering Journal, and is an Associate Editor of Information Sciences
Corresponding author: Q. L. Wei and X. Wang are with the State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, and with the School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, and also with Qingdao Academy of Intelligent Industries, Qingdao 266109, China (e-mail: qinglai.wei@ia.ac.cn; wangxin2019@ia.ac.cn)
Received Date: 2020-09-07
Accepted Date: 2020-10-01

Available Online: 2020-10-16

Abstract

Abstract

This paper investigates the consensus problem for linear multi-agent systems with the heterogeneous disturbances generated by the Brown motion. Its main contribution is that a control scheme is designed to achieve the dynamic consensus for the multi-agent systems in directed topology interfered by stochastic noise. In traditional ways, the coupling weights depending on the communication structure are static. A new distributed controller is designed based on Riccati inequalities, while updating the coupling weights associated with the gain matrix by state errors between adjacent agents. By introducing time-varying coupling weights into this novel control law, the state errors between leader and followers asymptotically converge to the minimum value utilizing the local interaction. Through the Lyapunov directed method and Itô formula, the stability of the closed-loop system with the proposed control law is analyzed. Two simulation results conducted by the new and traditional schemes are presented to demonstrate the effectiveness and advantage of the developed control method.
- Consensus control,
- directed topology,
- external disturbance,
- multi-agent (MA) systems

FullText(HTML)

References(41)

References

[1]	A. Jadbabaie, J. Lin, and A. S. Morse, “Coordination of groups of mobile autonomous agents using nearest neighbor rules,” IEEE Trans. Autom. Control, vol. 48, no. 6, pp. 988–1001, Jun. 2003. doi: 10.1109/TAC.2003.812781
[2]	T. Wang, M. Hu, and Y. L. Zhao, “Consensus control with a constant gain for discrete-time binary-valued multi-agent systems based on a projected empirical measure method,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 4, pp. 1052–1059, Jul. 2019. doi: 10.1109/JAS.2019.1911594
[3]	C. X. Shi, G. H. Yang, and X. J. Li, “Data-based fault-tolerant consensus control for uncertain multiagent systems via weighted edge dynamics,” IEEE Trans. Syst.,Man,Cybernet.:Syst., vol. 49, no. 12, pp. 2548–2558, Dec. 2019. doi: 10.1109/TSMC.2017.2743261
[4]	M. Yu, C. Song, F. C. Lv, K. Q. Jin, and W. Tan, “Event-triggered consensus approach for distributed battery energy storage systems,” IET Generat.,Trans. Distribut., vol. 13, no. 22, pp. 5102–5108, Nov. 2019. doi: 10.1049/iet-gtd.2018.6405
[5]	T. Vicsek, A. Czirok, E. Ben-Jacob, I. Cohen, and O. Shochet, “Novel type of phase transition in a system of self-driven particles,” Phys. Rev. Lett., vol. 75, no. 6, pp. 1226–1229, Aug. 1995. doi: 10.1103/PhysRevLett.75.1226
[6]	A. J. Wang, X. F. Liao, and H. B. He, “Event-triggered differentially private average consensus for multi-agent network,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 1, pp. 75–83, Jan. 2019. doi: 10.1109/JAS.2019.1911327
[7]	X. M. Li, Q. Zhou, P. S. Li, H. Y. Li, and R. Q. Lu, “Event-triggered consensus control for multi-agent systems against false data-injection attacks,” IEEE Trans. Cybernet., vol. 50, no. 5, pp. 1856–1866, May 2019.
[8]	Z. Wang, M. He, T. Zheng, Z. L. Fan, and G. B. Liu, “Guaranteed cost consensus for high-dimensional multi-agent systems with time-varying delays,” IEEE/CAA J. Autom. Sinica, vol. 5, no. 1, pp. 181–189, Jan. 2018. doi: 10.1109/JAS.2017.7510430
[9]	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, Sep. 2004. doi: 10.1109/TAC.2004.834113
[10]	J. X. Xi, C. Wang, H. Liu, and L. Wang, “Completely distributed guaranteed-performance consensualization for high-order multiagent systems with switching topologies,” IEEE Trans. Syst.,Man,Cybernet.:Syst., vol. 49, no. 7, pp. 1338–1348, Jul. 2019. doi: 10.1109/TSMC.2018.2852277
[11]	W. C. Huang, Y. W. Huang, and S. B. Chen, “Robust consensus control for a class of second-order multi-agent systems with uncertain topology and disturbances,” Neurocomputing, vol. 313, pp. 426–435, Nov. 2018. doi: 10.1016/j.neucom.2018.06.013
[12]	Q. K. Shen, P. Shi, J. W. Zhu, and L. P. Zhang, “Adaptive consensus control of leader-following systems with transmission nonlinearities,” Int. J. Control, vol. 92, no. 2, pp. 317–328, 2019. doi: 10.1080/00207179.2017.1352104
[13]	K. Li, C. C. Hua, X. You, and X. P. Guan, “Output feedback-based consensus control for nonlinear time delay multiagent systems,” Automatica, vol. 111, pp. 108669, Jan. 2020. doi: 10.1016/j.automatica.2019.108669
[14]	H. Y. Yang, F. Y. Wang, and F. J. Han, “Containment control of fractional order multi-agent systems with time delays,” IEEE/CAA J. Autom. Sinica, vol. 5, no. 3, pp. 727–732, May 2018. doi: 10.1109/JAS.2016.7510211
[15]	P. Ignaciuk and L Wieczorek, “Networked base-stock inventory control in complex distribution systems,” Mathem. Prob. Eng., vol. 2019, pp. 3754367, Nov. 2019.
[16]	J. Liu, Y. Yu, Q. Wang, and C. Y. Sun, “Fixed-time event-triggered consensus control for multi-agent systems with nonlinear uncertainties,” Neurocomputing, vol. 260, pp. 497–504, Oct. 2017. doi: 10.1016/j.neucom.2017.04.061
[17]	M. Yu, C. Yan, D. M. Xie, and G. M. Xie, “Event-triggered tracking consensus with packet losses and time-varying delays,” IEEE/CAA J. Autom. Sinica, vol. 3, no. 2, pp. 165–173, Apr. 2016. doi: 10.1109/JAS.2016.7451104
[18]	Q. L. Wei, H. Y. Li, and F. -Y. Wang, “Parallel control for continuous-time linear systems: A case study,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 4, pp. 919–928, Jul. 2020. doi: 10.1109/JAS.2020.1003216
[19]	Q. L. Wei, H. Y. Li, X. Yang, and H. B. He, “Continuous-time distributed policy iteration for multicontroller nonlinear systems,” IEEE Trans. Cybernet., Apr. 2020, DOI: 10.1109/TCYB.2020.2979614.
[20]	Q. L. Wei, L. X. Wang, Y. Liu, and M. M. Polycarpou, “Optimal elevator group control via deep asynchronous actor-critic learning,” IEEE Trans. Neural Netw. Learn. Syst., Feb. 2020, DOI: 10.1109/TNNLS.2020.2965208.
[21]	Q. L. Wei, Z. H. Liao, Z. Y. Yang, B. K. Li, and D. R. Liu, “Continuous-time time-varying policy iteration, ” IEEE Trans. Cybernet., Jul. 2019, DOI: 10.1109/TCYB.2019.2926631.
[22]	Q. L. Wei, R. Z. Song, Z. H. Liao, B. K. Li, and F. L. Lewis, “Discrete-time impulsive adaptive dynamic programming,” IEEE Trans. Cybernet., vol. 50, no. 10, pp. 4293–4306, Oct. 2020. doi: 10.1109/TCYB.2019.2906694
[23]	Q. L. Wei, D. R. Liu, and F. L. Lewis, “Optimal distributed synchronization control for continuous-time heterogeneous multi-agent differential graphical games,” Inf. Sci., vol. 317, pp. 96–113, Oct. 2015. doi: 10.1016/j.ins.2015.04.044
[24]	H. P. Zhang, D. Yue, C. X. Dou, W. Zhao, and X. P. Xie, “Data-driven distributed optimal consensus control for unknown multiagent systems with input-delay,” IEEE Trans. Cybernet., vol. 49, no. 6, pp. 2095–2105, Jun. 2019. doi: 10.1109/TCYB.2018.2819695
[25]	H. G. Zhang, L. Jiang, J. H. Liu, and F. M. Qu, “Data recovery of magnetic flux leakage data gaps using multifeature conditional risk,” IEEE Trans. Autom. Sci. Eng., Jun. 2020, DOI: 10.1109/TASE.2020.2994659.
[26]	H. G. Zhang, Y. Liu, and Y. C. Wang, “Observer-based finite-time adaptive fuzzy control for nontriangular nonlinear systems with full-state constraints,” IEEE Trans. Cybernet., Apr. 2020, DOI: 10.1109/TCYB.2020.2984791.
[27]	H. G. Zhang, Y. Liu, J. Dai, and Y. C. Wang, “Command filter based adaptive fuzzy finite-time control for a class of uncertain nonlinear systems with hysteresis,” IEEE Trans. Fuzzy Syst., Jun. 2020, DOI: 10.1109/TFUZZ.2020.3003499.
[28]	Z. K. Li, Z. S. Duan, G. R. Chen, and L. Huang, “Consensus of multiagent systems and synchronization of complex networks: a unified viewpoint,” IEEE Trans. Circuits Syst. I:Regular Papers, vol. 57, no. 1, pp. 213–224, Jan. 2010. doi: 10.1109/TCSI.2009.2023937
[29]	A. Elahi, A. Alfi, and H. Modares, “H consensus control of discretetime multi-agent systems under network imperfections and external disturbance,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 3, pp. 667–675, May 2019. doi: 10.1109/JAS.2019.1911474
[30]	X. X. Zhang and X. P. Liu, “Further results on consensus of second-order multi-agent systems with exogenous disturbance,” IEEE Trans. Circuits Syst. I:Regular Papers, vol. 60, no. 12, pp. 3215–3226, Dec. 2013. doi: 10.1109/TCSI.2013.2265978
[31]	H. Q. Lin, Q. L. Wei, D. R. Liu, and H. W. Ma, “Adaptive tracking control of leader-following linear multi-agent systems with external disturbances,” Int. J. Syst. Sci., vol. 47, no. 13, pp. 3167–3179, 2016. doi: 10.1080/00207721.2015.1102358
[32]	Y. Z. Lv, Z. K. Li, and Z. S. Duan, “Distributed pi control for consensus of heterogeneous multiagent systems over directed graphs,” IEEE Trans. Syst.,Man,Cybernet.:Syst., vol. 50, no. 4, pp. 1602–1609, Apr. 2020. doi: 10.1109/TSMC.2018.2792472
[33]	G. H. Wen, Z. S. Duan, G. R. Chen, and W. W. Yu, “Consensus tracking of multi-agent systems with lipschitz-type node dynamics and switching topologies,” IEEE Trans. Circuits Syst. I:Regular Papers, vol. 61, no. 2, pp. 499–511, Feb. 2014. doi: 10.1109/TCSI.2013.2268091
[34]	Z. K. Li, G. H. Wen, Z. S. Duan, and W. Ren, “Designing fully distributed consensus protocols for linear multi-agent systems with directed graphs,” IEEE Trans. Autom. Control, vol. 60, no. 4, pp. 1152–1157, Apr. 2015. doi: 10.1109/TAC.2014.2350391
[35]	R. A. Horn and C. R. Johnson, Matrix Analysis. Cambridge: Cambridge University Press, 2012.
[36]	X. Y. Yu, P. X. Ding, F. Yang, C. Zou, and L. L. Ou, “Stabilization parametric region of distributed PID controllers for general first-order multi-agent systems with time delay,” IEEE/CAA J. Autom. Sinica, pp. 1–10, Jul. 2019.
[37]	C. Huang, G. S. Zhai, and G. S. Xu, “Necessary and sufficient conditions for consensus in third order multi-agent systems,” IEEE/CAA J. Autom. Sinica, vol. 5, no. 6, pp. 1044–1053, Nov. 2018. doi: 10.1109/JAS.2018.7511222
[38]	S. Shi, S. Y. Xu, and H. Y. Feng, “Robust fixed-time consensus tracking control of high-order multiple nonholonomic systems,” IEEE Trans. Syst., Man, Cybernet.: Syst., Apr. 2019, DOI: 10.1109/TSMC.2019.2906902.
[39]	U. Hassler, “Ito’s lemma,” Stochastic Processes and Calculus, U. Hassler, Ed. Cham, Germany: Springer, 2016, pp. 239–258.
[40]	L. Cheng, Y. P. Wang, W. Ren, Z. G. Hou, and M. Tan, “On convergence rate of leader-following consensus of linear multi-agent systems with communication noises,” IEEE Trans. Autom. Control, vol. 61, no. 11, pp. 3586–3592, Nov. 2016. doi: 10.1109/TAC.2016.2522647
[41]	S. E. Tuna, “Conditions for synchronizability in arrays of coupled linear systems,” IEEE Trans. Autom. Control, vol. 54, no. 10, pp. 2416–2420, Oct. 2009. doi: 10.1109/TAC.2009.2029296

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)

Get Citation

PDF

XML

Article Metrics

Article views (1357) PDF downloads(145)

Highlights

A new distributed control law for multi-agent system is proposed.
The system is directed and inferred by external stochastic disturbance.
The proposed control law can solve the dynamic consensus problems.

Consensus Control of Leader-Following Multi-Agent Systems in Directed Topology With Heterogeneous Disturbances

doi: 10.1109/JAS.2021.1003838

Abstract

References

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Highlights

Export File

Citation

Format

Content