Human-in-the-Loop Consensus Control for Nonlinear Multi-Agent Systems With Actuator Faults

Guohuai Lin; Hongyi Li; Hui Ma; Deyin Yao; Renquan Lu

doi:10.1109/JAS.2020.1003596

Volume 9 Issue 1

Jan. 2022

IEEE/CAA Journal of Automatica Sinica

JCR Impact Factor: 11.8, Top 4% (SCI Q1)

CiteScore: 17.6, Top 3% (Q1)
Google Scholar h5-index: 77， TOP 5

Turn off MathJax

Article Contents

Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2022 > 9(1): 111-122

G. H. Lin, H. Y. Li, H. Ma, D. Y. Yao, and R. Q. Lu, “Human-in-the-loop consensus control for nonlinear multi-agent systems with actuator faults,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 1, pp. 111–122, Jan. 2022. doi: 10.1109/JAS.2020.1003596

Citation:

G. H. Lin, H. Y. Li, H. Ma, D. Y. Yao, and R. Q. Lu, “Human-in-the-loop consensus control for nonlinear multi-agent systems with actuator faults,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 1, pp. 111–122, Jan. 2022. doi: 10.1109/JAS.2020.1003596

Citation:

PDF( 2181 KB)

Human-in-the-Loop Consensus Control for Nonlinear Multi-Agent Systems With Actuator Faults

doi: 10.1109/JAS.2020.1003596

Guohuai Lin^1
,,
Hongyi Li^{1
,
,},
Hui Ma^1
,,
Deyin Yao^1
,,
Renquan Lu^1
,

School of Automation and Guangdong Province Key Laboratory of Intelligent Decision and Cooperative Control, Guangdong University of Technology, Guangzhou 510006, China

Funds: This work was partially supported by the National Natural Science Foundation of China (62033003, 62003098), the Local Innovative and Research Teams Project of Guangdong Special Support Program (2019BT02X353), and the China Postdoctoral Science Foundation (2019M662813, 2020T130124)

More Information

Author Bio:
Guohuai Lin received the B.S. degree in electrical engineering and automation from Guangdong University of Petrochemical Technology, in 2018. He is currently pursuing the Ph.D. degree in control science and engineering at Guangdong University of Technology. His current research interests include adaptive control and human-in-the-loop control for multi-agent systems

Hongyi Li (Senior Member, IEEE) received the Ph.D. degree in intelligent control from University of Portsmouth, Portsmouth, UK, in 2012. He was a Research Associate with the Department of Mechanical Engineering, University of Hong Kong, and Hong Kong Polytechnic University, China. He was a Visiting Principal Fellow with the Faculty of Engineering and Information Sciences, University of Wollongong, Wollongong, Australia. He is currently a Professor with Guangdong University of Technology. His research interests include intelligent control, cooperative control, sliding mode control and their applications. He was a recipient of the 2016 and 2019 Andrew P. Sage Best Transactions Paper Awards from IEEE System, Man, Cybernetics Society, the Best Paper Award in Theory from ICCSS 2017 and the Zadeh Best Student Paper from IEEE ICCSS 2019, respectively. He has been in the editorial board of several international journals, including IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Fuzzy Systems, IEEE Transactions on Systems, Man, and Cybernetics: Systems, IEEE Transactions on Cognitive and Developmental Systems, SCIENCE CHINA Information Sciences, IEEE/CAA Journal of Automatica Sinica, Journal of Systems Science and Complexity, Neural Networks, Asian Journal of Control, Circuits, Systems and Signal Processing, and International Journal of Control, Automation and Systems. He has been Guest Editors of IEEE Transactions on Cybernetics and IET Control Theory and Applications. He is a member of the IFAC Technical Committee on Computational Intelligence in Control

Hui Ma received the B.S. degree in mathematics from Harbin Normal University, in 2016, the M.S. degree in applied mathematics from Bohai University, in 2019. She is pursuing the Ph.D. degree in control science and engineering in Guangdong University of Technology. Her current research interests include fuzzy control and adaptive control for nonlinear systems

Deyin Yao received the M.S. and Ph.D. degrees in Control Theory from Bohai University, and School of Automation, Guangdong University of Technology, China, in 2016 and 2019, respectively. He is currently a Postdoctoral at the School of Automation, Guangdong University of Technology. From 2018 to 2019, he was a joint Ph.D. student with the Department of Mechanical Engineering, University of Victoria, Victoria, Canada. His current research interests include multi-agent systems, sliding mode control and Markov jump systems

Renquan Lu (Senior Member, IEEE) received the Ph.D. degree in control science and engineering from Zhejiang University, in 2004. He is currently a Professor with the School of Automation, Guangdong University of Technology. His research was supported by the National Science Fund for Distinguished Young Scientists of China in 2014, honored as the Distinguished Professor of Pearl River Scholars Program of Guangdong Province in 2015, and the Yangtze River Scholars Program by the Ministry of Education of China in 2017. His current research interests include complex systems, networked control systems, and nonlinear systems
Corresponding author: Hongyi Li, e-mail: lihongyi2009@gmail.com
Received Date: 2020-09-21
Revised Date: 2020-10-28
Accepted Date: 2020-11-03

Available Online: 2020-11-28

Abstract

Abstract

This paper considers the human-in-the-loop leader-following consensus control problem of multi-agent systems (MASs) with unknown matched nonlinear functions and actuator faults. It is assumed that a human operator controls the MASs via sending the command signal to a non-autonomous leader which generates the desired trajectory. Moreover, the leader’s input is nonzero and not available to all followers. By using neural networks and fault estimators to approximate unknown nonlinear dynamics and identify the actuator faults, respectively, the neighborhood observer-based neural fault-tolerant controller with dynamic coupling gains is designed. It is proved that the state of each follower can synchronize with the leader’s state under a directed graph and all signals in the closed-loop system are guaranteed to be cooperatively uniformly ultimately bounded. Finally, simulation results are presented for verifying the effectiveness of the proposed control method.
- Actuator faults,
- distributed control,
- human-in-the-loop,
- neighborhood observer,
- nonlinear multi-agent systems (MASs)

FullText(HTML)

References(54)

References

[1]	J. A. Fax and R. M. Murray, “Information flow and cooperative control of vehicle formations,” IEEE Trans. Autom. Control, vol. 49, no. 9, pp. 1465–1476, Sep. 2004. doi: 10.1109/TAC.2004.834433
[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. Y. Chen, R. Yu, Y. Zhang, and C. L. Liu, “Circular formation flight control for unmanned aerial vehicles with directed network and external disturbance,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 2, pp. 505–516, Mar. 2020. doi: 10.1109/JAS.2019.1911669
[4]	Z. Y. Gao and G. Guo, “Fixed-time sliding mode formation control of AUVs based on a disturbance observer,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 2, pp. 539–545, Mar. 2020. doi: 10.1109/JAS.2020.1003057
[5]	Q. L. Yang, J. Sun, and J. Chen, “Output consensus for heterogeneous linear multiagent systems with a predictive event-triggered mechanism,” IEEE Trans. Cybern., vol. 51, no. 4, pp. 1993–2005, Apr. 2021. doi: 10.1109/TCYB.2019.2895044
[6]	Y. X. Su, Q. L. Wang, and C. Y. Sun, “Self-triggered consensus control for linear multi-agent systems with input saturation,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 1, pp. 150–157, Jan. 2020. doi: 10.1109/JAS.2019.1911837
[7]	Z. K. Li, W. Ren, X. D. Liu, and M. Y. Fu, “Consensus of multi-agent systems with general linear and Lipschitz nonlinear dynamics using distributed adaptive protocols,” IEEE Trans. Autom. Control, vol. 58, no. 7, pp. 1786–1791, Jul. 2013. doi: 10.1109/TAC.2012.2235715
[8]	Q. S. Liu and J. Wang, “A second-order multi-agent network for bound-constrained distributed optimization,” IEEE Trans. Autom. Control, vol. 60, no. 12, pp. 3310–3315, Dec. 2015. doi: 10.1109/TAC.2015.2416927
[9]	H. W. Zhang, F. L. Lewis, and A. Das, “Optimal design for synchronization of cooperative systems: State feedback, observer and output feedback,” IEEE Trans. Autom. Control, vol. 56, no. 8, pp. 1948–1952, Aug. 2011. doi: 10.1109/TAC.2011.2139510
[10]	Y. Y. Qian, L. Liu, and G. Feng, “Distributed event-triggered adaptive control for consensus of linear multi-agent systems with external disturbances,” IEEE Trans. Cybern., vol. 50, no. 5, pp. 2197–2208, May 2020. doi: 10.1109/TCYB.2018.2881484
[11]	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
[12]	W. B. Xiao, L. Cao, H. Y. Li, and R. Q. Lu, “Observer-based adaptive consensus control for nonlinear multi-agent systems with time-delay,” Sci. China Inf. Sci., vol. 63, no. 3, Article No. 132202, Feb. 2020. doi: 10.1007/s11432-019-2678-2
[13]	Q. Zhou, G. D. Chen, R. Q. Lu, and W. W. Bai, “Disturbance-observer-based event-triggered control for multi-agent systems with input saturation,” Sci. Sinica Inform., vol. 49, no. 11, pp. 1502–1516, Nov. 2019.
[14]	G. W. Dong, H. Y. Li, H. Ma, and R. Q. Lu, “Finite-time consensus tracking neural network FTC of multi-agent systems,” IEEE Trans. Neural Netw. Learn. Syst., vol. 32, no. 2, pp. 653–662, Nov. 2021. doi: 10.1109/TNNLS.2020.2978898
[15]	Q. Zhou, W. Wang, H. J. Liang, M. V. Basin, and B. H. Wang, “Observer-based event-triggered fuzzy adaptive bipartite containment control of multiagent systems with input quantization,” IEEE Trans. Fuzzy Syst., vol. 29, no. 2, pp. 372–384, Feb. 2021. doi: 10.1109/TFUZZ.2019.2953573
[16]	W. Wang, H. J. Liang, Y. N. Pan, and T. S. Li, “Prescribed performance adaptive fuzzy containment control for nonlinear multiagent systems using disturbance observer,” IEEE Trans. Cybern., vol. 50, no. 9, pp. 3879–3891, Sep. 2020. doi: 10.1109/TCYB.2020.2969499
[17]	W. C. Zou, P. Shi, Z. R. Xiang, and Y. Shi, “Consensus tracking control of switched stochastic nonlinear multiagent systems via event-triggered strategy,” IEEE Trans. Neural Netw. Learn. Syst., vol. 31, no. 3, pp. 1036–1045, Mar. 2020. doi: 10.1109/TNNLS.2019.2917137
[18]	A. K. Jain and S. Bhasin, “Tracking control of uncertain nonlinear systems with unknown constant input delay,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 2, pp. 420–425, Mar. 2020. doi: 10.1109/JAS.2019.1911807
[19]	Q. Zhou, S. Y. Zhao, H. Y. Li, R. Q. Lu, and C. W. Wu, “Adaptive neural network tracking control for robotic manipulators with dead zone,” IEEE Trans. Neural Netw. Learn. Syst., vol. 30, no. 12, pp. 3611–3620, Dec. 2019. doi: 10.1109/TNNLS.2018.2869375
[20]	C. E. Ren, C. L. P. Chen, T. Du, and Y. Guan, “Fuzzy adaptive leader-following consensus control for nonlinear multi-agent systems with unknown control directions,” Int. J. Fuzzy Syst., vol. 21, no. 7, pp. 2066–2076, Aug. 2019. doi: 10.1007/s40815-019-00710-1
[21]	M. H. Zhang and X. J. Jing, “A bioinspired dynamics-based adaptive fuzzy SMC method for half-car active suspension systems with input dead zones and saturations,” IEEE Trans. Cybern., vol. 51, no. 4, pp. 1743–1755, Apr. 2021. doi: 10.1109/TCYB.2020.2972322
[22]	W. W. Bai, T. S. Li, and S. C. Tong, “NN reinforcement learning adaptive control for a class of nonstrict-feedback discrete-time systems,” IEEE Trans. Cybern., vol. 50, no. 11, pp. 4573–4584, Nov. 2020. doi: 10.1109/TCYB.2020.2963849
[23]	Z. T. Li, L. X. Gao, W. H. Chen, and Y. Xu, “Distributed adaptive cooperative tracking of uncertain nonlinear fractional-order multi-agent systems,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 1, pp. 292–300, Jan. 2020. doi: 10.1109/JAS.2019.1911858
[24]	H. Ma, H. Y. Li, R. Q. Lu, and T. W. Huang, “Adaptive event-triggered control for a class of nonlinear systems with periodic disturbances,” Sci. China Inf. Sci., vol. 63, no. 5, Article No. 150212, Mar. 2020. doi: 10.1007/s11432-019-2680-1
[25]	L. Feng, C. Wiltsche, L. Humphrey, and U. Topcu, “Synthesis of human-in-the-loop control protocols for autonomous systems,” IEEE Trans. Autom. Sci. Eng., vol. 13, no. 2, pp. 450–462, Apr. 2016. doi: 10.1109/TASE.2016.2530623
[26]	B. Kiumarsi and T. Başar, “Human-in-the-loop control of distributed multi-agent systems: A relative input-output approach,” in Proc. IEEE Conf. Decision and Control, Miami, USA, 2018, pp. 3343−3348.
[27]	A. T. Koru, T. Yucelen, R. Sipahi, A. Ramĺrez, and K. M. Dogan, “Stability of human-in-the-loop multiagent systems with time delays,” in Proc. American Control Conf., Philadelphia, USA, 2019, pp. 4854−4859.
[28]	T. Hatanaka, N. Chopra, and M. Fujita, “Passivity-based bilateral human-swarm-interactions for cooperative robotic networks and human passivity analysis,” in Proc. 54th IEEE Conf. Decision and Control, Osaka, Japan, 2015, pp. 1033−1039.
[29]	Y. C. Chang, “Architecture design for performing grasp-and-lift tasks in brain-machine-interface-based human-in-the-loop robotic system,” IET Cyber-Phys. Syst.:Theory Appl., vol. 4, no. 3, pp. 198–203, Sep. 2019. doi: 10.1049/iet-cps.2018.5066
[30]	M. Inoue and V. Gupta, ““Weak” control for human-in-the-loop systems,” IEEE Control Syst. Lett., vol. 3, no. 2, pp. 440–445, Apr. 2019. doi: 10.1109/LCSYS.2019.2891489
[31]	B. Jiang, M. Staroswiecki, and V. Cocquempot, “Fault accommodation for nonlinear dynamic systems,” IEEE Trans. Autom. Control, vol. 51, no. 9, pp. 1578–1583, Sep. 2006. doi: 10.1109/TAC.2006.878732
[32]	D. Ye, M. M. Chen, and H. J. Yang, “Distributed adaptive event-triggered fault-tolerant consensus of multiagent systems with general linear dynamics,” IEEE Trans. Cybern., vol. 49, no. 3, pp. 757–767, Mar. 2019. doi: 10.1109/TCYB.2017.2782731
[33]	P. Gong, W. Y. Lan, and Q. L. Han, “Robust adaptive fault-tolerant consensus control for uncertain nonlinear fractional-order multi-agent systems with directed topologies,” Automatica, vol. 117, Article No. 109011, Jul. 2020. doi: 10.1016/j.automatica.2020.109011
[34]	C. Deng and G. H. Yang, “Adaptive fault-tolerant control for a class of nonlinear multi-agent systems with actuator faults,” J. Franklin Inst., vol. 354, no. 12, pp. 4784–4800, Aug. 2017. doi: 10.1016/j.jfranklin.2017.05.034
[35]	Y. J. Wang, Y. D. Song, and F. L. Lewis, “Robust adaptive fault-tolerant control of multiagent systems with uncertain nonidentical dynamics and undetectable actuation failures,” IEEE Trans. Ind. Electron., vol. 62, no. 6, pp. 3978–3988, Jun. 2015.
[36]	S. Chen, D. W. C. Ho, L. L. Li, and M. Liu, “Fault-tolerant consensus of multi-agent system with distributed adaptive protocol,” IEEE Trans. Cybern., vol. 45, no. 10, pp. 2142–2155, Oct. 2015. doi: 10.1109/TCYB.2014.2366204
[37]	W. Wang, D. Wang, and Z. H. Peng, “Cooperative fuzzy adaptive output feedback control for synchronisation of nonlinear multi-agent systems under directed graphs,” Int. J. Syst. Sci., vol. 46, no. 16, pp. 2982–2995, Dec. 2015. doi: 10.1080/00207721.2014.886135
[38]	T. S. Li, Z. F. Li, D. Wang, and C. L. P. Chen, “Output-feedback adaptive neural control for stochastic nonlinear time-varying delay systems with unknown control directions,” IEEE Trans. Neural Netw. Learn. Syst., vol. 26, no. 6, pp. 1188–1201, Jun. 2015. doi: 10.1109/TNNLS.2014.2334638
[39]	H. J. Liang, X. Y. Guo, Y. N. Pan, and T. W. Huang, “Event-triggered fuzzy bipartite tracking control for network systems based on distributed reduced-order observers,” IEEE Trans. Fuzzy Syst., vol. 29, no. 6, pp. 1601–1614, Jun. 2021. doi: 10.1109/TFUZZ.2020.2982618
[40]	J. Mao, H. R. Karimi, and Z. R. Xiang, “Observer-based adaptive consensus for a class of nonlinear multiagent systems,” IEEE Trans. Syst.,Man,Cybern.:Syst., vol. 49, no. 9, pp. 1893–1900, Sep. 2019. doi: 10.1109/TSMC.2017.2776219
[41]	Y. Liu, X. P. Liu, Y. W. Jing, H. Q. Wang, and X. H. Li, “Annular domain finite-time connective control for large-scale systems with expanding construction,” IEEE Trans. Syst., Man, Cybern.: Syst., to be published. doi: 10.1109/TSMC.2019.2960009
[42]	L. J. Wang and C. L. P. Chen, “Reduced-order observer-based dynamic event-triggered adaptive NN control for stochastic nonlinear systems subject to unknown input saturation,” IEEE Trans. Neural Netw. Learn. Syst., vol. 32, no. 4, pp. 1678–1690, Apr. 2021. doi: 10.1109/TNNLS.2020.2986281
[43]	S. C. Tong, X. Min, and Y. X. Li, “Observer-based adaptive fuzzy tracking control for strict-feedback nonlinear systems with unknown control gain functions,” IEEE Trans. Cybern., vol. 50, no. 9, pp. 3903–3913, Sep. 2020. doi: 10.1109/TCYB.2020.2977175
[44]	Z. K. Li, W. Ren, X. D. Liu, and L. H. Xie, “Distributed consensus of linear multi-agent systems with adaptive dynamic protocols,” Automatica, vol. 49, no. 7, pp. 1986–1995, Jul. 2013. doi: 10.1016/j.automatica.2013.03.015
[45]	Y. Z. Lv, Z. K. Li, Z. S. Duan, and J. Chen, “Distributed adaptive output feedback consensus protocols for linear systems on directed graphs with a leader of bounded input,” Automatica, vol. 74, pp. 308–314, Dec. 2016. doi: 10.1016/j.automatica.2016.07.041
[46]	S. Khoo, L. H. Xie, and Z. H. Man, “Robust finite-time consensus tracking algorithm for multirobot systems,” IEEE/ASME Trans. Mechatronics, vol. 14, no. 2, pp. 219–228, Apr. 2009. doi: 10.1109/TMECH.2009.2014057
[47]	W. B. Zhang, Y. Tang, Y. R. Liu, and J. Kurths, “Event-triggering containment control for a class of multi-agent networks with fixed and switching topologies,” IEEE Trans. Circuits Syst. I:Reg. Pap., vol. 64, no. 3, pp. 619–629, Mar. 2017. doi: 10.1109/TCSI.2016.2618944
[48]	Y. G. Hong, G. R. Chen, and L. Bushnell, “Distributed observers design for leader-following control of multi-agent networks,” Automatica, vol. 44, no. 3, pp. 846–850, Mar. 2008. doi: 10.1016/j.automatica.2007.07.004
[49]	R. Cui, B. Ren, and S. S. Ge, “Synchronised tracking control of multi-agent system with high-order dynamics,” IET Control Theory Appl., vol. 6, no. 5, pp. 603–614, Mar. 2012. doi: 10.1049/iet-cta.2011.0011
[50]	Z. Y. Jia, L. L. Wang, J. Q. Yu, and X. L. Ai, “Distributed adaptive neural networks leader-following formation control for quadrotors with directed switching topologies,” ISA Trans., vol. 93, pp. 93–107, Oct. 2019. doi: 10.1016/j.isatra.2019.02.030
[51]	H. R. Ren, R. Q. Lu, J. L. Xiong, and Y. Xu, “Optimal estimation for discrete-time linear system with communication constraints and measurement quantization,” IEEE Trans. Syst.,Man,Cybern.:Syst., vol. 50, no. 5, pp. 1932–1942, May 2020. doi: 10.1109/TSMC.2018.2792009
[52]	Y. B. Huang, Y. He, J. Q. An, and M. Wu, “Polynomial-type Lyapunov-Krasovskii functional and Jacobi-Bessel inequality: Further results on stability analysis of time-delay systems,” IEEE Trans. Autom. Control, vol. 66, no. 6, pp. 2905–2912, Jun. 2020. doi: 10.1109/TAC.2020.3013930
[53]	C. K. Zhang, F. Long, Y. He, W. Yao, L. Jiang, and M. Wu, “A relaxed quadratic function negative-determination lemma and its application to time-delay systems,” Automatica, vol. 113, Article No. 108764, Mar. 2020. doi: 10.1016/j.automatica.2019.108764
[54]	H. R. Ren, H. R. Karimi, R. Q. Lu, and Y. Q. Wu, “Synchronization of network systems via aperiodic sampled-data control with constant delay and application to unmanned ground vehicles,” IEEE Trans. Ind. Electron., vol. 67, no. 6, pp. 4980–4990, Jun. 2020. doi: 10.1109/TIE.2019.2928241

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(9)

Get Citation

PDF

XML

Article Metrics

Article views (1219) PDF downloads(165)

Highlights

In this paper, the leader of the nonlinear MASs is considered as non-autonomous and the leader’s control input is time-varying, which is provided by the human operator. In addition, we remove the restriction assumption that a subset of followers can access the leader’s control input
Different from most existing results, the controller designed in this paper achieves the leader-following consensus via adaptive coupling strengths for online adjustment. Furthermore, the considered nonlinear MASs are more general than the high-order Brunovsky form nonlinear systems
By using the relative information of neighboring nodes, the neighborhood observer is designed to estimate the unmeasurable states of nonlinear MASs, and the neighborhood observer-based neural fault-tolerant controller with dynamic coupling gains is constructed to achieve the leader-following consensus of MASs

Human-in-the-Loop Consensus Control for Nonlinear Multi-Agent Systems With Actuator Faults

doi: 10.1109/JAS.2020.1003596

Abstract

References

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Highlights

Export File

Citation

Format

Content