Prescribed Performance Bipartite Consensus Control for MASs Under Data-Driven Strategy

Qi Zhou; Caiyun Yin; Hui Ma; Hongru Ren; Hongyi Li

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

Article Contents

Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2024 > In Press, Accepted Manuscript

Q. Zhou, C. Yin, H. Ma, H. Ren, and H. Li, “Prescribed performance bipartite consensus control for MASs under data-driven strategy,” IEEE/CAA J. Autom. Sinica..

Citation:

Q. Zhou, C. Yin, H. Ma, H. Ren, and H. Li, “Prescribed performance bipartite consensus control for MASs under data-driven strategy,” IEEE/CAA J. Autom. Sinica..

Q. Zhou, C. Yin, H. Ma, H. Ren, and H. Li, “Prescribed performance bipartite consensus control for MASs under data-driven strategy,” IEEE/CAA J. Autom. Sinica..

Citation:

Q. Zhou, C. Yin, H. Ma, H. Ren, and H. Li, “Prescribed performance bipartite consensus control for MASs under data-driven strategy,” IEEE/CAA J. Autom. Sinica..

PDF( 1355 KB)

Prescribed Performance Bipartite Consensus Control for MASs Under Data-Driven Strategy

Funds: This work was supported in part by the National Natural Science Foundation of China (62373113, 62433014, 62433018) and the Guangdong Basic and Applied Basic Research Foundation (2023A1515011527, 2023B1515120010)

More Information

Author Bio:
Qi Zhou received the B.S. and M.S. degrees in mathematics from Bohai University in 2006 and 2009, respectively, and the Ph.D. degree in control science and engineering from the Nanjing University of Science and Technology in 2013. Her research interests include adaptive control, fuzzy-logic control, neural control, and robust control for nonlinear systems

Caiyun Yin received the B.S. degree in automation from Hunan University of Science and Technology in 2021, and the M.S. degree in control science and engineering from the Guangdong University of Technology. Her research interests include nonlinear systems, model-free adaptive control, and data-driven 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, and the Ph.D. degree in control science and engineering from the Guangdong University of Technology in 2021. From 2022 to 2023, she was a Postdoctoral Fellow with the School of Mathematics and Statistics, Guangdong University of Technology. She is currently with the School of Mathematics and Statistics, Guangdong University of Technology. Her research interests include nonlinear systems, adaptive neural network control, and adaptive fuzzy control. She was the recipient of the fellowship of China National Postdoctoral Program for Innovative Talents

Hongru Ren (Member, IEEE) received the B.E. and Ph.D. degrees in control science and engineering from the University of Science and Technology of China in 2013 and 2019, respectively. His research interests include complex nonlinear systems, networked control systems, and unmanned autonomous systems

Hongyi Li (Senior Member, IEEE) received the Ph.D. degree in intelligent control from the University of Portsmouth, U.K in 2012. He was a Research Associate with the Department of Mechanical Engineering, University of Hong Kong, China, and Hong Kong Polytechnic University, China. He was a Visiting Principal Fellow with the Faculty of Engineering and Information Sciences, University of Wollongong, Australia. He is currently a Professor with the Southwest University. His research interests include intelligent control, cooperative control and their applications. Prof. Li was the 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 Zadeh Best Student Paper from ICCSS 2019. He was on 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 Automation Science and Engineering, IEEE Transactions on Cognitive and Developmental Systems, SCIENCE CHINA Information Sciences, IEEE/CAA Journal of Automatica Sinica, Journal of Systems Science and Complexity, and Asian Journal of Control. He was the Guest Editor of IEEE Transactions on Cybernetics and IET Control Theory and Applications
Corresponding author: Qi Zhou, e-mail: zhouqi2009@gdut.edu.cn
Received Date: 2024-07-31
Accepted Date: 2024-09-27

Available Online: 2025-03-21

Abstract

Abstract

This paper investigates the bipartite consensus control problem for discrete time nonlinear multiagent systems (MASs) based on data-driven adaptive method. To begin with, a dynamic linearization strategy is utilized to establish the relationship between bipartite tracking error and control input for MASs. Secondly, the unknown parameter linearly associated with control input is acquired by the adaptive control approach, and a discrete time extended state observer is designed to estimate nonlinear uncertainties. Thirdly, in order to achieve the prescribed performance, the constrained bipartite consensus error is transformed through a strictly increasing function. Based on the converted equivalent unconstrained error function, a sliding mode controller using only the input and output data of the MASs is designed. Finally, the efficacy of the controller is confirmed by simulations.
- Data-driven,
- nonlinear multiagent systems (MASs),
- prescribed performance,
- sliding mode control

FullText(HTML)

References(50)

References

[1]	L. Ma, Y.-L. Wang, and Q.-L. Han, “Cooperative target tracking of multiple autonomous surface vehicles under switching interaction topologies,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 3, pp. 673–684, 2022.
[2]	B. Lian, W. Xue, F. L. Lewis, and T. Chai, “Inverse reinforcement learning for multi-player noncooperative apprentice games,” Automatica, vol. 145, p. 110524, 2022. doi: 10.1016/j.automatica.2022.110524
[3]	S. Yang, Y. Pan, L. Cao, and L. Chen, “Predefined-time fault-tolerant consensus tracking control for multi-UAV systems with prescribed performance and attitude constraints,” IEEE Trans. Aerospace and Electronic Systems, 2024. doi: 10.1109/TAES.2024.3371406,2024
[4]	Y. Ren and W. Sun, “Robust adaptive control for robotic systems with input time-varying delay using hamiltonian method,” IEEE/CAA J. Autom. Sinica, vol. 5, no. 4, pp. 852–859, 2016.
[5]	Z. Guo, H. Li, H. Ma, and W. Meng, “Distributed optimal attitude synchronization control of multiple QUAVs via adaptive dynamic programming,” IEEE Trans. Neural Networks and Learning Systems, 2022. doi: 10.1109/TNNLS.2022.3224029,2022
[6]	S.-L. Dai, S. He, H. Cai, and C. Yang, “Adaptive leader–follower formation control of underactuated surface vehicles with guaranteed performance,” IEEE Trans. Systems, Man, and Cybern.: Systems, vol. 52, no. 3, pp. 1997–2008, 2020.
[7]	J. Sun, Y. Yan, H. Zhang, and M. Shao, “Consensus–fuzzy ecological joint therapy for multi-tumor populations,” IEEE Trans. Fuzzy Systems, 2023. doi: 10.1109/TFUZZ.2023.3305007,2023
[8]	J. Liang, X. Bu, L. Cui, and Z. 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, 2022.
[9]	H. Ren, H. Ma, H. Li, and Z. Wang, “Adaptive fixed-time control of nonlinear MASs with actuator faults,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 5, pp. 1252–1262, 2023. doi: 10.1109/JAS.2023.123558
[10]	J. Sun and Z. Ming, “Cooperative differential game-based distributed optimal synchronization control of heterogeneous nonlinear multiagent systems,” IEEE Trans. Cybern., vol. 53, no. 12, pp. 7933–7942, 2023. doi: 10.1109/TCYB.2023.3240983
[11]	H. Li, J. Luo, H. Ma, and Q. Zhou, “Observer-based event-triggered iterative learning consensus for locally Lipschitz nonlinear MASs,” IEEE Trans. Cognitive and Developmental Systems, vol. 16, no. 1, pp. 46–56, 2023.
[12]	Z. Chen, “Synchronization of frequency modulated multi-agent systems,” IEEE Trans. Autom. Control, vol. 68, no. 6, pp. 3425–3439, 2023. doi: 10.1109/TAC.2022.3197125
[13]	H. Ren, Z. Liu, H. Liang, and H. Li, “Pinning-based neural control for multiagent systems with self-regulation intermediate event-triggered method,” IEEE Trans. Neural Networks and Learning Systems, 2024. doi: 10.1109/TNNLS.2024.3386881,2024
[14]	L. Yuan, T. Jiang, L. Li, F. Chen, Z. Zhang, and Y. Yu, “Robust cooperative multi-agent reinforcement learning via multi-view message certification,” Science China Infor. Sciences, vol. 67, no. 4, p. 142102, 2024. doi: 10.1007/s11432-023-3853-y
[15]	X. Guo, C. Wang, and L. Liu, “Adaptive fault-tolerant control for a class of nonlinear multi-agent systems with multiple unknown time-varying control directions,” Automatica, vol. 167, p. 111802, 2024. doi: 10.1016/j.automatica.2024.111802
[16]	A. Luo, Q. Zhou, H. Ma, and H. Li, “Observer-based consensus control for MASs with prescribed constraints via reinforcement learning algorithm,” IEEE Trans. Neural Networks and Learning Systems, 2023. doi: 10.1109/TNNLS.2023.3301538,2023
[17]	T.-F. Ding, M.-F. Ge, C.-H. Xiong, and J. H. Park, “Bipartite consensus for networked robotic systems with quantized-data interactions,” Information Sciences, vol. 511, pp. 229–242, 2020. doi: 10.1016/j.ins.2019.09.046
[18]	B. Ning, Q. Han, and Z. Zuo, “Bipartite consensus tracking for second-order multiagent systems: A time-varying function-based preset-time approach,” IEEE Trans. Autom. Control, vol. 66, no. 6, pp. 2739–2745, 2021. doi: 10.1109/TAC.2020.3008125
[19]	L. Chen, L. Shi, Q. Zhou, H. Sheng, and Y. 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, 2022. doi: 10.1109/JAS.2022.105758
[20]	S. Xiong and Z. Hou, “Data-driven formation control for unknown MIMO nonlinear discrete-time multi-agent systems with sensor fault,” IEEE Trans. Neural Networks and Learning Systems, vol. 33, no. 12, pp. 7728–7742, 2021.
[21]	H. Ma, H. Ren, Q. Zhou, H. Li, and Z. Wang, “Observer-based neural control of N-link flexible-joint robots,” IEEE Trans. Neural Networks and Learning Systems, 2022. doi: 10.1109/TNNLS.2022.3203074,2022
[22]	X. Zheng, H. Li, C. K. Ahn, and D. Yao, “NN-based fixed-time attitude tracking control for multiple unmanned aerial vehicles with nonlinear faults,” IEEE Trans. Aerospace and Electronic Systems, vol. 59, no. 2, pp. 1738–1748, 2022.
[23]	X. Yu, Z. Hou, M. M. Polycarpou, and L. Duan, “Data-driven iterative learning control for nonlinear discrete-time MIMO systems,” IEEE Trans. Neural Networks and Learning Systems, vol. 32, no. 3, pp. 1136–1148, 2021. doi: 10.1109/TNNLS.2020.2980588
[24]	R. Chi, Y. Hui, S. Zhang, B. Huang, and Z. Hou, “Discrete-time extended state observer-based model-free adaptive control via local dynamic linearization,” IEEE Trans. Industrial Electronics, vol. 67, no. 10, pp. 8691–8701, 2020. doi: 10.1109/TIE.2019.2947873
[25]	W. Cao, J. Yan, X. Yang, X. Luo, and X. Guan, “Model-free formation control of autonomous underwater vehicles: A broad learning-based solution,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 5, pp. 1325–1328, 2023. doi: 10.1109/JAS.2023.123165
[26]	Q. Zhou, Q. Ren, H. Ma, G. Chen, and H. Li, “Model-free adaptive control for nonlinear systems under dynamic sparse attacks and measurement disturbances,” IEEE Trans. Circuits and Systems–I: Regular Papers, 2024. doi: 10.1109/TCSI.2024.3434607,2024
[27]	D. Liu and G.-H. Yang, “Prescribed performance model-free adaptive integral sliding mode control for discrete-time nonlinear systems,” IEEE Trans. Neural Networks and Learning Systems, vol. 30, no. 7, pp. 2222–2230, 2018.
[28]	H. Zhao, J. Shan, L. Peng, and H. Yu, “Distributed event-triggered bipartite consensus for multiagent systems against injection attacks,” IEEE Trans. Industrial Informatics, vol. 19, no. 4, pp. 5377–5386, 2022.
[29]	R. Chi, X. Guo, N. Lin, and B. Huang, “Dynamic linearization and extended state observer-based data-driven adaptive control,” IEEE Trans. Systems, Man, and Cybern.: Systems, vol. 53, no. 11, pp. 6805–6814, 2023. doi: 10.1109/TSMC.2023.3286575
[30]	Y. Hui, R. Chi, B. Huang, Z. Hou, and S. Jin, “Observer-based sampled-data model-free adaptive control for continuous-time nonlinear nonaffine systems with input rate constraints,” IEEE Trans. Systems, Man, and Cybern.: Systems, vol. 51, no. 12, pp. 7813–7822, 2021. doi: 10.1109/TSMC.2020.2982491
[31]	Y. Hui, R. Chi, B. Huang, and Z. Hou, “Data-driven adaptive iterative learning bipartite consensus for heterogeneous nonlinear cooperation-antagonism networks,” IEEE Trans. Neural Networks and Learning Systems, 2022. doi: 10.1109/TNNLS.2022.3148726,2022
[32]	J. Liang, X. Bu, L. Cui, and Z. Hou, “Data-driven bipartite formation for a class of nonlinear MIMO multiagent systems,” IEEE Trans. Neural Networks and Learning Systems, 2021. doi: 10.1109/TNNLS.2021.3111893,2021
[33]	H. Zhao, H. Yu, and L. Peng, “Event-triggered distributed data-driven iterative learning bipartite formation control for unknown nonlinear multiagent systems,” IEEE Trans. Neural Networks and Learning Systems, 2022. doi: 10.1109/TNNLS.2022.3174885,2022
[34]	Y.-S. Ma, W. Che, C. Deng, and Z. Wu, “Distributed model-free adaptive control for learning nonlinear MASs under DoS attacks,” IEEE Trans. Neural Networks and Learning Systems, 2021. doi: 10.1109/TNNLS.2021.3104978,2021
[35]	Y. Yang, J. Tan, and D. Yue, “Prescribed performance control of one-dof link manipulator with uncertainties and input saturation constraint,” IEEE/CAA Journal of Automatica Sinica, vol. 6, no. 1, pp. 148–157, 2018.
[36]	D. Liu and G.-H. Yang, “Data-driven adaptive sliding mode control of nonlinear discrete-time systems with prescribed performance,” IEEE Trans. Systems, Man, and Cybern.: Systems, vol. 49, no. 12, pp. 2598–2604, 2019. doi: 10.1109/TSMC.2017.2779564
[37]	——, “Prescribed performance model-free adaptive integral sliding mode control for discrete-time nonlinear systems,” IEEE Trans. Neural Networks and Learning Systems, vol. 30, no. 7, pp. 2222–2230, 2019. doi: 10.1109/TNNLS.2018.2881205
[38]	W. Zhang, D. Xu, B. Jiang, and T. Pan, “Prescribed performance based model-free adaptive sliding mode constrained control for a class of nonlinear systems,” Information Sciences, vol. 544, pp. 97–116, 2021. doi: 10.1016/j.ins.2020.06.061
[39]	J. She, K. Miyamoto, Q.-L. Han, M. Wu, H. Hashimoto, and Q.-G. Wang, “Generalized-extended-state-observer and equivalent-input-disturbance methods for active disturbance rejection: Deep observation and comparison,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 4, pp. 957–968, 2022.
[40]	M. L. Nguyen, X. Chen, and F. Yang, “Discrete-time quasi-sliding-mode control with prescribed performance function and its application to piezo-actuated positioning systems,” IEEE Trans. Industrial Electronics, vol. 65, no. 1, pp. 942–950, 2018. doi: 10.1109/TIE.2017.2708024
[41]	Y.-J. Liu and H. Chen, “Adaptive sliding mode control for uncertain active suspension systems with prescribed performance,” IEEE Trans. Systems, Man, and Cybern.: Systems, vol. 51, no. 10, pp. 6414–6422, 2021. doi: 10.1109/TSMC.2019.2961927
[42]	D. Liu, Z.-P. Zhou, and T.-S. Li, “Data-driven bipartite consensus tracking for nonlinear multiagent systems with prescribed performance,” IEEE Trans. Systems, Man, and Cybern.: Systems, vol. 53, no. 6, pp. 3666–3674, 2023. doi: 10.1109/TSMC.2022.3230504
[43]	Y. Zhang and J. Song, “Nonlinear leader-following MASs control: A data-driven adaptive sliding mode approach with prescribed performance,” Nonlinear Dynamics, vol. 108, no. 1, pp. 349–361, 2022. doi: 10.1007/s11071-022-07218-8
[44]	Z. Hou and S. Jin, “A novel data-driven control approach for a class of discrete-time nonlinear systems,” IEEE Trans. Control Systems Technology, vol. 19, no. 6, pp. 1549–1558, 2011. doi: 10.1109/TCST.2010.2093136
[45]	R. Chi, Y. Hui, B. Huang, Z. Hou, and X. Bu, “Data-driven adaptive consensus learning from network topologies,” IEEE Trans. Neural Networks and Learning Systems, vol. 33, no. 8, pp. 3487–3497, 2021.
[46]	D. Meng, Y. Jia, and J. Du, “Robust consensus tracking control for multiagent systems with initial state shifts, disturbances, and switching topologies,” IEEE Trans. Neural Networks and Learning Systems, vol. 26, no. 4, pp. 809–824, 2015. doi: 10.1109/TNNLS.2014.2327214
[47]	Z. Wang, C. Mu, S. Hu, C. Chu, and X. Li, “Modeling the dynamics of regret minimization in large agent populations: A master equation approach,” in Proc. IJCAI, 2022, pp. 534–540.
[48]	Y. Liu, Y. Fu, W. He, and Q. Hui, “Modeling and observer-based vibration control of a flexible spacecraft with external disturbances,” IEEE Trans. Industrial Electronics, vol. 66, no. 11, pp. 8648–8658, 2018.
[49]	X.-M. Zhang, Q.-L. Han, B.-L. Zhang, X. Ge, and D. Zhang, “Accumulated-state-error-based event-triggered sampling scheme and its application to $ {H}_{ {\infty }}$ control of sampled-data systems,” Science China Information Sciences, vol. 67, no. 6, p. 162206, 2024. doi: 10.1007/s11432-023-4038-3
[50]	Y. Zhu, Z. Zhang, C. Xia, and Z. Chen, “Equilibrium analysis and incentive-based control of the anticoordinating networked game dynamics,” Automatica, vol. 147, p. 110707, 2023. doi: 10.1016/j.automatica.2022.110707

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)

Get Citation

PDF

XML

Article Metrics

Article views (17) PDF downloads(13)

Prescribed Performance Bipartite Consensus Control for MASs Under Data-Driven Strategy

Abstract

References

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Export File

Citation

Format

Content