Data-Driven Control of Distributed Event-Triggered Network Systems

Xin Wang; Jian Sun; Gang Wang; Frank Allgöwer; Jie Chen

doi:10.1109/JAS.2023.123225

Volume 10 Issue 2

Feb. 2023

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 > 2023 > 10(2): 351-364

X. Wang, J. Sun, G. Wang, F. Allgöwer, and J. Chen, “Data-driven control of distributed event-triggered network systems,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 2, pp. 351–364, Feb. 2023. doi: 10.1109/JAS.2023.123225

Citation:

X. Wang, J. Sun, G. Wang, F. Allgöwer, and J. Chen, “Data-driven control of distributed event-triggered network systems,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 2, pp. 351–364, Feb. 2023. doi: 10.1109/JAS.2023.123225

Citation:

PDF( 1604 KB)

Data-Driven Control of Distributed Event-Triggered Network Systems

doi: 10.1109/JAS.2023.123225

Xin Wang^1
,,
Jian Sun^1
,,
Gang Wang^{1
,
,},
Frank Allgöwer^2
,,
Jie Chen^3
,

1.
National Key Laboratory of Autonomous Intelligent Unmanned Systems, School of Automation, Beijing Institute of Technology, Beijing 100081, and also with the Beijing Institute of Technology Chongqing Innovation Center, Chongqing 401120, China
2.
Institute for Systems Theory and Automatic Control, University of Stuttgart, Stuttgart 70550, Germany
3.
Department of Control Science and Engineering, Tongji University, Shanghai 201804, and also with the National Key Laboratory of Autonomous Intelligent Unmanned Systems, Beijing Institute of Technology, Beijing 100081, China

Funds: This work was supported in part by the National Key Research and Development Program of China (2021YFB1714800), the National Natural Science Foundation of China (62088101, 61925303, 62173034, U20B2073), the Natural Science Foundation of Chongqing (2021ZX4100027), and the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germanys Excellence Strategy— EXC 2075-390740016 (468094890)

More Information

Author Bio:
Xin Wang received the master degree from the College of Electrical and Information Engineering, Hunan University of Technology, in 2018. Currently, he is pursuing the Ph.D. degree at the School of Automation, Beijing Institute of Technology. His research interests include even-triggered and data-driven control systems

Jian Sun (Senior Member, IEEE) received the bachelor degree in electric engineering from Jilin Institute of Technology, in 2001, the master degree in mechatronical engineering from Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences (CAS), in 2004, and the Ph.D. degree in control theory and control engineering from the Institute of Automation, CAS, in 2007.He was a Research Fellow with the Faculty of Advanced Technology, University of Glamorgan, Pontypridd, UK, from 2008 to 2009. He was a Post-Doctoral Research Fellow with Beijing Institute of Technology, from 2007 to 2010. In 2010, he joined the School of Automation, Beijing Institute of Technology, where he has been a Professor since 2013. His current research interests include networked control systems, time-delay systems, and security of cyber-physical systems. Dr. Sun is an Editorial Board Member of IEEE Transactions on Systems, Man, and Cybernetics: Systems, Journal of Systems Science & Complexity, and Acta Automatica Sinica

Gang Wang (Member, IEEE) received the B.Eng. degree in automatic control in 2011, and the Ph.D. degree in control science and engineering in 2018, both from Beijing Institute of Technology. He also received the Ph.D. degree in electrical and computer engineering from the University of Minnesota, USA, in 2018, where he stayed as a Postdoctoral Researcher until July 2020. Since August 2020, he has been a Professor with the School of Automation, Beijing Institute of Technology.His research interests include signal processing, control, and reinforcement learning with applications to cyber-physical systems and multi-agent systems. He was the recipient of the IEEE CSS Beijing Chapter Young Author Prize in 2022, the Best Paper Award from the Frontiers of Information Technology & Electronic Engineering (FITEE) in 2021, the Excellent Doctoral Dissertation Award from the Chinese Association of Automation in 2019, the Best Student Paper Award from the 2017 European Signal Processing Conference, and the Best Conference Paper at the 2019 IEEE Power & Energy Society General Meeting. He is currently an Associate Editor of Signal Processing and the IEEE Transactions on Signal and Information Processing over Networks, as well as an Early Career Advisory Board member of the IEEE/CAA Journal of Automatica Sinica

Frank Allgöwer is a Professor of mechanical engineering at the University of Stuttgart, Germany, and Director of the Institute for Systems Theory and Automatic Control (IST) there.He is active in serving the community in several roles: Among others he has been President of the International Federation of Automatic Control (IFAC) for the years 2017–2020, Vice-president for Technical Activities of the IEEE Control Systems Society for 2013/14, and Editor of the Journal Automatica from 2001 until 2015. From 2012 until 2020 Frank served in addition as Vice-President for the German Research Foundation (DFG), which is Germany’s most important research funding organization.His research interests include predictive control, data-based control, networked control, cooperative control, and nonlinear control with application to a wide range of fields including systems biology

Jie Chen (Fellow, IEEE) received the B.Sc., M.Sc., and Ph.D. degrees in control theory and control engineering from Beijing Institute of Technology, in 1986, 1996, and 2001, respectively. From 1989 to 1990, he was a visiting scholar at the California State University, USA. From 1996 to 1997, he was a Research Fellow in the School of Engineering at the University of Birmingham, UK.He is a Professor with the School of Automation, Beijing Institute of Technology, where he serves as the Director of the Key Laboratory of Intelligent Control and Decision of Complex Systems. He also serves as the President of Tongji University. His research interests include complex systems, multiagent systems, multiobjective optimization and decision, and constrained nonlinear control. Prof. Chen is currently the Editor-in-Chief of Unmanned Systems, Autonomous Intelligent Systems, and the Journal of Systems Science and Complexity. He has served on the editorial boards of several journals, including the IEEE Transactions on Cybernetics, International Journal of Robust and Nonlinear Control, and Science China Information Sciences. He is a Fellow of IEEE, IFAC, and a Member of the Chinese Academy of Engineering
Corresponding author: Gang Wang, e-mail: gangwang@bit.edu.cn
Received Date: 2022-06-04
Accepted Date: 2022-08-20

Available Online: 2022-10-26

Abstract

Abstract

The present paper deals with data-driven event-triggered control of a class of unknown discrete-time interconnected systems (a.k.a. network systems). To this end, we start by putting forth a novel distributed event-triggering transmission strategy based on periodic sampling, under which a model-based stability criterion for the closed-loop network system is derived, by leveraging a discrete-time looped-functional approach. Marrying the model-based criterion with a data-driven system representation recently developed in the literature, a purely data-driven stability criterion expressed in the form of linear matrix inequalities (LMIs) is established. Meanwhile, the data-driven stability criterion suggests a means for co-designing the event-triggering coefficient matrix and the feedback control gain matrix using only some offline collected state-input data. Finally, numerical results corroborate the efficacy of the proposed distributed data-driven event-triggerednetwork system (ETS) in cutting off data transmissions and the co-design procedure.
- Data-driven control,
- distributed event-triggered network system (ETS),
- linear matrix inequalitie (LMI),
- looped-functional,
- stability

FullText(HTML)

References(41)

References

[1]	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
[2]	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
[3]	K. Bansal and P. Mukhija, “Aperiodic sampled-data control of distributed networked control systems under stochastic cyber-attacks,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 4, pp. 1064–1073, Jul. 2020. doi: 10.1109/JAS.2020.1003249
[4]	J. Chen, J. Sun, and G. Wang, “From unmanned systems to autonomous intelligent systems,” Engineering, vol. 12, no. 5, pp. 16–19, 2022.
[5]	Y. L. Ding, B. Xin, and J. Chen, “A review of recent advances in coordination between unmanned aerial and ground vehicles,” Unmanned Syst., vol. 9, no. 2, pp. 97–117, Apr. 2021. doi: 10.1142/S2301385021500084
[6]	X. Wang, J. Sun, G. Wang, and L. Dou, “Improved stability conditions for time-varying delay systems via relaxed Lyapunov functionals,” Int. J. Control, 2022. [Online]. Available: http://dx.doi.org/10.1080/00207179.2022.2056716.
[7]	W. P. Heemels, K. H. Johansson, and P. Tabuada, “An introduction to event-triggered and self-triggered control,” in Proc. 51st IEEE Conf. Decision and Control. Maui, USA, 2012, pp. 3270–3285.
[8]	I. Ahmad, X. H. Ge, and Q.-L. Han, “Decentralized dynamic event-triggered communication and active suspension control of in-wheel motor driven electric vehicles with dynamic damping,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 5, pp. 971–986, Apr. 2021. doi: 10.1109/JAS.2021.1003967
[9]	M. C. F. Donkers and W. P. M. H. Heemels, “Output-based event-triggered control with guaranteed $ {\cal{L}}_{\infty}$-gain and improved and decentralized event-triggering” IEEE Trans. Autom. Control, vol. 57, no. 6, pp. 1362–1376, Jun. 2012. doi: 10.1109/TAC.2011.2174696
[10]	Z. Y. Chen, Q.-L. Han, Y. M. Yan, and Z.-G. Wu, “How often should one update control and estimation: Review of networked triggering techniques,” Sci. China Inf. Sci., vol. 63, no. 5, pp. 2–19, May 2020.
[11]	Y. Chen, M. Z. Zhu, R. Q. Lu, and A. K. Xue, “Distributed H_∞ filtering of nonlinear systems with random topology by an event-triggered protocol,” Sci. China Inf. Sci., vol. 64, no. 10, pp. 202–216, Oct. 2021.
[12]	X. Wang, J. Sun, G. Wang, and L. H. Dou, “A mixed switching event-triggered transmission scheme for networked control systems,” IEEE Trans. Control Netw. Syst., vol. 9, no. 1, pp. 390–402, Mar. 2022. doi: 10.1109/TCNS.2021.3106447
[13]	X. Wang and M. D. Lemmon, “Event-triggering in distributed networked control systems,” IEEE Trans. Autom. Control, vol. 56, no. 3, pp. 586–601, Jul. 2011. doi: 10.1109/TAC.2010.2057951
[14]	Z. X. Wang, M. R. Fei, D. J. Du, and M. Zheng, “Decentralized event-triggered average consensus for multi-agent systems in CPSs with communication constraints,” IEEE/CAA J. Autom. Sinica, vol. 2, no. 3, pp. 248–257, Jul. 2015. doi: 10.1109/JAS.2015.7152658
[15]	T. Shi, T. T. Tang, and J. J. Bai, “Distributed event-triggered control co-design for large-scale systems via static output feedback,” J. Frankl. Inst., vol. 356, no. 17, pp. 10393–10404, Nov. 2019. doi: 10.1016/j.jfranklin.2018.05.051
[16]	C. Deng, W. W. Che, and Z. G. Wu, “A dynamic periodic event-triggered approach to consensus of heterogeneous linear multiagent systems with time-varying communication delays,” IEEE Trans. Cybern., vol. 51, no. 4, pp. 1812–1821, Sept. 2021. doi: 10.1109/TCYB.2020.3015746
[17]	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
[18]	K. J. Åström and B. Wittenmark, “On self-tuning regulators,” Automatica, vol. 9, no. 2, pp. 185–199, Mar. 1973. doi: 10.1016/0005-1098(73)90073-3
[19]	Z. S. Hou and Z. Wang, “From model-based control to data-driven control: Survey, classification and perspective,” IEEE Trans. Ind. Inform., vol. 235, pp. 3–35, Jun. 2013.
[20]	I. Markovsky and F. Dörfler, “Behavioral systems theory in data-driven analysis, signal processing, and control,” Annu. Rev. Control, vol. 52, pp. 42–64, Jan. 2021. doi: 10.1016/j.arcontrol.2021.09.005
[21]	L. Ren, T. Wang, Y. J. Laili, and L. Zhang, “A data-driven self-supervised LSTM-DeepFM model for industrial soft sensor,” IEEE Trans. Ind. Inf., vol. 18, no. 9, pp. 5859–5869, Sept. 2022. doi: 10.1109/TII.2021.3131471.
[22]	J. Eising and J. Cortés, “Informativity for centralized design of distributed controllers for networked systems,” arXiv: 2207.14770, Jul. 2022.
[23]	H. J. van Waarde, M. K. Camlibel, and M. Mesbahi, “From noisy data to feedback controllers: Nonconservative design via a matrix S-lemma,” IEEE Trans. Autom. Control, vol. 67, no. 1, pp. 162–175, Jan. 2022. doi: 10.1109/TAC.2020.3047577
[24]	E. Fridman, “A refined input delay approach to sampled-data control,” Automatica, vol. 46, no. 2, pp. 421–427, Feb. 2010. doi: 10.1016/j.automatica.2009.11.017
[25]	J. Berberich, S. Wildhagen, M. Hertneck, and F. Allgöwer, “Data-driven analysis and control of continuous-time systems under aperiodic sampling,” IFAC-PapersOnLine, vol. 54, no. 7, pp. 210–215, Jul. 2021. doi: 10.1016/j.ifacol.2021.08.360
[26]	S. Wildhagen, J. Berberich, M. Hertneck, and F. Allgöwer, “Data-driven analysis and controller design for discrete-time systems under aperiodic sampling,” IEEE Trans. Autom. Control, pp. 1–16, Jun. 2022. DOI: 10.1109/TAC.2022.3183969
[27]	W. Liu, J. Sun, G. Wang, F. Bullo, and J. Chen, “Data-driven resilient predictive control under denial-of-service,” IEEE Trans. Autom. Control, pp. 1–13, 2022. [Online]. Available: https://dx.doi.org/10.1109/TAC.2022.3209399.
[28]	X. Wang, J. Sun, J. Berberich, G. Wang, F. Allgöwer, and J. Chen, “Data-driven control of dynamic event-triggered systems with delays,” Int. J. Robust Nonlin. Control, pp. 1–20, 2022. [Online]. Available: https://arxiv.org/abs/2110.12768.
[29]	X. Wang, J. Berberich, J. Sun, G. Wang, F. Allgöwer, and J. Chen, “Model-based and data-driven control of event- and self-triggered discrete-time LTI systems”. IEEE Trans. Cybern, pp. 1–14, 2022. [Online]. Available: https://arxiv.org/abs/2202.08019
[30]	A. Seuret, “A novel stability analysis of linear systems under asynchronous samplings,” Automatica, vol. 48, no. 1, pp. 177–182, Jan. 2012. doi: 10.1016/j.automatica.2011.09.033
[31]	Y. P. Guan, G. N. Ping, W. X. Zheng, and H. J. Yao, “Distributed event-triggered scheduling in networked interconnected systems with sparse connections,” Neurocomputing, vol. 400, pp. 420–428, Aug. 2020. doi: 10.1016/j.neucom.2019.04.080
[32]	C. Nowzari, E. Garcia, and J. Cortés, “Event-triggered communication and control of networked systems for multi-agent consensus,” Automatica, vol. 105, pp. 1–27, 2019. doi: 10.1016/j.automatica.2019.03.009
[33]	C. De Persis and P. Tesi, “Formulas for data-driven control: Stabilization, optimality, and robustness,” IEEE Trans. Autom. Control, vol. 65, no. 3, pp. 909–924, Mar. 2020. doi: 10.1109/TAC.2019.2959924
[34]	A. Girard, “Dynamic triggering mechanisms for event-triggered control,” IEEE Trans. Autom. Control, vol. 60, no. 7, pp. 1992–1997, Jul. 2015. doi: 10.1109/TAC.2014.2366855
[35]	R. K. Mishra and H. Ishii, “Dynamic event-triggered consensus control of discrete-time linear multi-agent systems,” IFAC-PapersOnLine, vol. 54, no. 17, pp. 123–128, Sept. 2021. doi: 10.1016/j.ifacol.2021.11.036
[36]	J. Chen, S. Y. Xu, X. L. Jia, and B. Y. Zhang, “Novel summation inequalities and their applications to stability analysis for systems with time-varying delay,” IEEE Trans. Autom. Control, vol. 62, no. 5, pp. 2470–2475, Sept. 2017. doi: 10.1109/TAC.2016.2606902
[37]	X. Wang, J. Sun, and L. Dou, “Improved results on stability analysis of sampled-data systems,” Int. J. Robust Nonlin. Control, vol. 31, no. 14, pp. 6549–6561, Sept. 2021. doi: 10.1002/rnc.5633
[38]	C. W. Scherer, “LPV control and full block multipliers,” Automatica, vol. 37, no. 3, pp. 361–375, Mar. 2001. doi: 10.1016/S0005-1098(00)00176-X
[39]	J. Park and P. Park, “An extended looped-functional for stability analysis of sampled-data systems,” Int. J. Robust Nonlin. Control, vol. 30, no. 18, pp. 7962–7969, Dec. 2020. doi: 10.1002/rnc.5221
[40]	J. Berberich, C. W. Scherer, and F. Allgöwer, “Combining prior knowledge and data for robust controller design,” arXiv: 2009.05253, Sept. 2020.
[41]	J. F. Sturm, “Using SeDuMi 1.02, a MATLAB toolbox for optimization over symmetric cones,” Optim. Methods Softw., vol. 11, no. 1, pp. 625–653, Mar. 1999.

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(8) / Tables(2)

Get Citation

PDF

XML

Article Metrics

Article views (979) PDF downloads(320)

Highlights

A data-driven event-triggered control of a class of unknown discrete-time interconnected systems (a.k.a. network systems) using purely input-state data
A novel dynamic distributed event-triggered transmission scheme only involving the discrete-time state information of each subsystem and its neighbor(s) at sampling instants
Model-based and data-driven methods for co-designing the controller and the event-triggering matrices, based on discrete-time looped-functional approach

Data-Driven Control of Distributed Event-Triggered Network Systems

doi: 10.1109/JAS.2023.123225

Abstract

References

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Highlights

Export File

Citation

Format

Content