Event-Triggered Robust Parallel Optimal Consensus Control for Multiagent Systems

Qinglai Wei; Shanshan Jiao; Qi Dong; Fei-Yue Wang

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2025 > In Press, Accepted Manuscript

Q. Wei, S. Jiao, Q. Dong, and F.-Y. Wang, “Event-triggered robust parallel optimal consensus control for multiagent systems,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 1, pp. 1–14, Jan. 2025.

Citation:

Q. Wei, S. Jiao, Q. Dong, and F.-Y. Wang, “Event-triggered robust parallel optimal consensus control for multiagent systems,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 1, pp. 1–14, Jan. 2025.

Q. Wei, S. Jiao, Q. Dong, and F.-Y. Wang, “Event-triggered robust parallel optimal consensus control for multiagent systems,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 1, pp. 1–14, Jan. 2025.

Citation:

Q. Wei, S. Jiao, Q. Dong, and F.-Y. Wang, “Event-triggered robust parallel optimal consensus control for multiagent systems,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 1, pp. 1–14, Jan. 2025.

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Event-Triggered Robust Parallel Optimal Consensus Control for Multiagent Systems

Funds: This work was supported in part by the National Key Research and Development Program of China (2021YFE0206100), the National Natural Science Foundation of China (62425310, 62073321), the National Defense Basic Scientific Research Program (JCKY2019203C029, JCKY2020130C025), and the Science and Technology Development Fund, Macau SAR (FDCT-22-009-MISE, 0060/2021/A20015/2020/AMJ)

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Author Bio:
Qinglai Wei (Senior Member, IEEE) 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 Post-Doctoral 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 at the Institute and an Associate Director at the Laboratory. He has authored four books, and published over 80 international journal articles. His research interests include adaptive dynamic programming, neural networks-based control, optimal control, nonlinear systems, and their industrial applications. He was a recipient of the IEEE/CAA Journal of Automatica Sinica Best Paper Award, the IEEE System, Man, and Cybernetics Society Andrew P. Sage Best Transactions Paper Award, the IEEE Transactions on Neural Networks and Learning Systems Outstanding Paper Award, the Outstanding Paper Award of Acta Automatica Sinica, and the Zhang Siying Outstanding Paper Award of the Chinese Control and Decision Conference (CCDC). He was also a recipient of the Shuang-Chuang Talents in Jiangsu Province, China, and the Young Researcher Award of Asia-Pacific Neural Network Society (APNNS). He has been the Vice President of the IEEE Computational Intelligence Society’s (CIS) Beijing Chapter since 2022. He was the General Co-Chair of the 2019 Symposium of Parallel Intelligence, the Invited Session Chair of the 8th IEEE Data Driven Control and Learning Systems Conference, the Special Sessions Chair of the 25th International Conference on Neural Information Processing, the Program Co-Chair of the 24th International Conference on Neural Information Processing, and the Registration Chair of the 12th World Congress on Intelligent Control and Automation (WCICA2016) and the 2014 IEEE World Congress on Computational Intelligence (WCCI2014). He was a Guest Editor for several international journals. He is the Associate Editor-in-Chief/the Deputy Editor-in-Chief of the IEEE/CAA Journal of Automatica Sinica, Neurocomputing, and Acta Automatica Sinica. He is also an Associate/Senior Editor of IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Automation Science and Engineering, IEEE Transactions on Cognitive and Developmental Systems, abd IEEE Transactions on Systems, Man, and Cybernetics: Systems

Shanshan Jiao received the bachelor degree in electronic and information engineering from Ankang University, in 2018, and the master degree in control theory and control engineering from Xi’an University of Technology in 2021. She is currently pursuing the Ph.D. degree in intelligent science and systems with the Institute of Systems Engineering, Macau University of Science and Technology, Macau, China. Her research interests include optimal control, robust control, adaptive dynamic programming, and neural-networks-based control

Qi Dong received the Ph.D. degree in control science and engineering from Tianjin University in 2017. He is the Deputy Director of the Quantum Technology Innovation Center at China Academic of Electronics and Information Technology. He is also an expert in the field of game theory at China Electronics Technology Group Corporation. He serves as the Chief Engineer of a key project under a national defense technology program. He is the project leader for the Ministry of Industry and Information Technology’s “Leading the Charge” project in the field of artificial intelligence. He is engaged in theoretical and technical research on swarm intelligence unmanned systems. Prof. Dong has received numerous honors and awards, including the “Outstanding Ph.D. Dissertation Award” from the Chinese Institute of Electronics, selection for the fourth “Yong Elite Scientists Sponsorship Program by CAST”, an “Outstanding Paper Award” from the IEEE Navigation, International Conference on Guidance, Navigation and Control. He was awarded the ninth “Wu Wenjun AI Outstanding Youth Award” in 2019, and inclusion in the “Top Ten Scientific and Technological Innovation Teams” of China Electronics Technology Group Corporation. In 2022, he was honored as an “Outstanding Communist Party Member” by China Academic of Electronics and Information Technology. Dong Qi holds various positions in professional organizations, including Executive Committee Member in Intelligent Game and Military Chess Deduction Committee of Chinese Institute of Command and Control, Member of the Technical Committee on Unmanned Aerial Vehicle Autonomous Control of Chinese Association of Automation, and Young Committee Member of the Technical Committee on Guidance, Navigation and Control(TCGNC), CSAA. He also serves as an editorial board member for journals such as Chinese Journal of Engineering and CAAI Transactions on Intelligent Systems

Fei-Yue Wang (Fellow, IEEE) received the Ph.D. degree in computer and systems engineering from Rensselaer Polytechnic Institute, Troy, USA, in 1990. He joined the University of Arizona, Tucson, USA, in 1990 and became a Professor and the Director of the Robotics and Automation Laboratory and the Program in Advanced Research for Complex Systems. In 1999, he founded the Intelligent Control and Systems Engineering Center, Institute of Automation, Chinese Academy of Sciences (CAS), under the support of the Outstanding Chinese Talents Program from the State Planning Council, and in 2002, was appointed as the Director of the Key Laboratory of Complex Systems and Intelligence Science, CAS. In 2011, he became the State Specially Appointed Expert and the Director of the State Key Laboratory for Management and Control of Complex Systems. His current research interests include methods and applications for parallel intelligence, social computing, and knowledge automation. Prof. Wang received the National Prize in Natural Sciences of China and became an Outstanding Scientist of ACM for his work in intelligent control and social computing in 2007, the IEEE ITS Outstanding Application and Research Awards in 2009 and 2011, respectively, and the IEEE Norbert Wiener Award in 2014. Since 1997, he has been serving as the General or Program Chair of over 30 IEEE, INFORMS, IFAC, ACM, and ASME conferences. He was the President of the IEEE ITS Society from 2005 to 2007, the Chinese Association for Science and Technology, USA, in 2005, and the American Zhu Kezhen Education Foundation from 2007 to 2008, the Vice President of the ACM China Council from 2010 to 2011, and the Vice President and the Secretary General of the Chinese Association of Automation from 2008 to 2018. He was the Founding Editor-in-Chief (EiC) of the International Journal of Intelligent Control and Systems from 1995 to 2000, the IEEE Intelligent Transportation Systems Magazine from 2006 to 2007, the IEEE/CAA Journal of Automatica Sinica from 2014 to 2017, and the China’s Journal of Command and Control from 2015 to 2020. He was the EiC of the IEEE Intelligent Systems from 2009 to 2012, the IEEE Transactions on Intelligent Transportation Systems from 2009 to 2016, and the IEEE Transactions on Computational Social Systems from 2017 to 2020, and has been the Founding EiC of Chinese Journal of Intelligent Science and Technology since 2019 and the EiC of the IEEE Transactions on Intelligent Vehicles since 2022. He is currently the President of CAA’s Supervision Council, IEEE Council on RFID, and the Vice President of the IEEE Systems, Man, and Cybernetics Society. He is a Fellow of INCOSE, IFAC, ASME, and AAAS
Corresponding author: Qinglai Wei, e-mail: qinglai.wei@ia.ac.cn
Received Date: 2024-02-10
Accepted Date: 2024-07-20

Available Online: 2024-10-16

Abstract

Abstract

This paper highlights the utilization of parallel control and adaptive dynamic programming (ADP) for event-triggered robust parallel optimal consensus control (ETRPOC) of uncertain nonlinear continuous-time multiagent systems (MASs). First, the parallel control system, which consists of a virtual control variable and a specific auxiliary variable obtained from the coupled Hamiltonian, allows general systems to be transformed into affine systems. Of interest is the fact that the parallel control technique’s introduction provides an unprecedented perspective on eliminating the negative effects of disturbance. Then, an event-triggered mechanism is adopted to save communication resources while ensuring the system’s stability. The coupled Hamilton-Jacobi (HJ) equation’s solution is approximated using a critic neural network (NN), whose weights are updated in response to events. Furthermore, theoretical analysis reveals that the weight estimation error is uniformly ultimately bounded (UUB). Finally, numerical simulations demonstrate the effectiveness of the developed ETRPOC method.
- Adaptive dynamic programming (ADP),
- critic neural network (NN),
- event-triggered control,
- optimal consensus control,
- robust control

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References

[1]	J. Wang, Y. Hong, J. Wang, J. Xu, Y. Tang, Q.-L. Han, and J. Kurths, “Cooperative and competitive multi-agent systems: From optimization to games,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 5, pp. 763–783, May 2022. doi: 10.1109/JAS.2022.105506
[2]	J. Liu, Y. Wu, M. Sun, and C. Sun, “Fixed-time cooperative tracking for delayed disturbed multi-agent systems under dynamic event-triggered control,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 5, pp. 930–933, May 2022. doi: 10.1109/JAS.2022.105503
[3]	L. Wang, D. Zhu, W. Pang, and C. Luo, “A novel obstacle avoidance consensus control for Multi-AUV formation system,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 5, pp. 1304–1318, May 2023. doi: 10.1109/JAS.2023.123201
[4]	S. He, X. Liu, P. Lu, H. Liu, and C. Du, “Leader-follower finite-time consensus of multiagent systems with nonlinear dynamics by intermittent protocol,” J. Frankl. Inst., vol. 359, no. 6, pp. 2646–2662, Apr. 2022. doi: 10.1016/j.jfranklin.2022.01.031
[5]	S. Jiao, Q. Wei, and F.-Y. Wang, “A novel parallel control method for optimal consensus of nonlinear multiagent systems,” IEEE Trans. Cybern., vol. 54, no. 10, pp. 5912−5925, Oct. 2024.
[6]	Y. Lv, Z. Li, and Z. Duan, “Distributed PI control for consensus of heterogeneous multiagent systems over directed graphs,” IEEE Trans. Syst., Man, Cybern., Syst., vol. 50, no. 4, pp. 1602–1609, Apr. 2020. doi: 10.1109/TSMC.2018.2792472
[7]	D. Qi, J. Hu, X. Liang, J. Zhang, and Z. Zhang, “Research on consensus of multi-agent systems with and without input saturation constraints,” J. Syst. Eng. Electron., vol. 32, no. 4, pp. 947–955, Aug. 2021. doi: 10.23919/JSEE.2021.000081
[8]	K. Hengster-Movric, K. You, F. L. Lewis, and L. Xie, “Synchronization of discrete-time multi-agent systems on graphs using Riccati design,” Automatica, vol. 49, no. 2, pp. 414–423, Feb. 2013. doi: 10.1016/j.automatica.2012.11.038
[9]	T. Feng, J. Zhang, Y. Tong, and H. Zhang, “Consensusability and global optimality of discrete-time linear multiagent systems,” IEEE Trans. Cybern., vol. 52, no. 8, pp. 8227–8238, Aug. 2022. doi: 10.1109/TCYB.2021.3049910
[10]	X. Zhang, L. Liu, and G. Feng, “Leader-follower consensus of time-varying nonlinear multi-agent systems,” Automatica, vol. 52, pp. 8–14, Feb. 2015. doi: 10.1016/j.automatica.2014.10.127
[11]	C. Wang, X. Wang, and H. Ji, “A continuous leader-following consensus control strategy for a class of uncertain multi-agent systems,” IEEE/CAA J. Autom. Sinica, vol. 1, no. 2, pp. 187–192, Apr. 2014. doi: 10.1109/JAS.2014.7004549
[12]	H. L. Trentelman, K. Takaba, and N. Monshizadeh, “Robust synchronization of uncertain linear multi-agent systems,” IEEE Trans. Autom. Control, vol. 58, no. 6, pp. 1511–1523, Jun. 2013. doi: 10.1109/TAC.2013.2239011
[13]	X. Wang, Y. Hong, J. Huang, and Z.-P. Jiang, “A distributed control approach to a robust output regulation problem for multi-agent linear systems,” IEEE Trans. Autom. Control, vol. 55, no. 12, pp. 2891–2895, Dec. 2010. doi: 10.1109/TAC.2010.2076250
[14]	Y. Su, Y. Hong, and J. Huang, “A general result on the robust cooperative output regulation for linear uncertain multi-agent systems,” IEEE Trans. Autom. Control, vol. 58, no. 5, pp. 1275–1279, Dec. 2013. doi: 10.1109/TAC.2012.2229837
[15]	P. Yu, K. Liu, X. Liu, X. Li, M. Wu, and J. She, “Robust consensus tracking control of uncertain multi-agent systems with local disturbance rejection,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 2, pp. 427–438, Feb. 2023. doi: 10.1109/JAS.2023.123231
[16]	S. Khankalantary, I. Izadi, and F. Sheikholeslam, “Robust ADP-based solution of a class of nonlinear multi-agent systems with input saturation and collision avoidance constraints,” ISA Trans., vol. 107, pp. 52–62, Dec. 2020. doi: 10.1016/j.isatra.2020.07.029
[17]	Y. Guo and G. Chen, “Robust near-optimal coordination in uncertain multiagent networks with motion constraints,” IEEE Trans. Cybern., vol. 53, no. 5, pp. 2841–2851, May 2023. doi: 10.1109/TCYB.2021.3125318
[18]	Z. Zhang, S. Zhang, H. Li, and W. Yan, “Cooperative robust optimal control of uncertain multi-agent systems,” J. Frankl. Inst., vol. 357, no. 14, pp. 9467–9483, Sept. 2020. doi: 10.1016/j.jfranklin.2020.07.021
[19]	G. Guo and R. Zhang, “Lyapunov redesign-based optimal consensus control for multi-agent systems with uncertain dynamics,” IEEE Trans. Circuits Syst. Ⅱ: Exp. Briefs, vol. 69, no. 6, pp. 2902–2906, Jun. 2022. doi: 10.1109/TCSII.2022.3149911
[20]	M. A. Khalid, M. Rehan, F. Tahir, and M. Hussain, “Robust leader-following consensus control of one-sided Lipschitz multi-agent systems over heterogeneous matching uncertainties,” Results in Control and Optimization, vol. 8, p. 100151, Sept. 2022. doi: 10.1016/j.rico.2022.100151
[21]	F.-Y. Wang, “Parallel systen methods for management and control of complex systems,” Control and Decision, vol. 19, no. 5, pp. 485–489, Jan. 2004.
[22]	F.-Y. Wang, “Parallel control and management for intelligent transportation systems: Concepts, architectures, and applications,” IEEE Trans. Intell. Transp. Syst., vol. 11, no. 3, pp. 630–638, Sept. 2010. doi: 10.1109/TITS.2010.2060218
[23]	R. Song, L. Liu, and B. Hu, “Aperiodic sampling artificial-actual H_∞ optimal control for interconnected constrained systems,” IEEE Trans. Autom. Sci. Eng., pp. 1–11, Oct. 2023.
[24]	F.-Y. Wang, N.-N. Zheng, D. Cao, C. M. Martinez, L. Li, and T. Liu, “Parallel driving in CPSS: a unified approach for transport automation and vehicle intelligence,” IEEE/CAA J. Autom. Sinica, vol. 4, no. 4, pp. 577–587, Sept. 2017. doi: 10.1109/JAS.2017.7510598
[25]	W. Zhang, K. Wang, Y. Liu, Y. Lu, and F.-Y. Wang, “A parallel vision approach to scene-specific pedestrian detection,” Neurocomputing, vol. 394, pp. 114–126, Jun. 2020. doi: 10.1016/j.neucom.2019.03.095
[26]	D. Li and J. Dong, “Performance-constrained fault-tolerant DSC based on reinforcement learning for nonlinear systems with uncertain parameters,” Appl. Math. Comput., vol. 443, p. 127759, Apr. 2023.
[27]	S. Jiao, Q. Wei, and J. Xiao, “Parallel control with event-based adaptive critic implementation for robust optimal tracking of uncertain nonlinear systems,” IEEE Trans. Autom. Sci. Eng., pp. 1–14, Jul. 2024.
[28]	Q. Wei, S. Jiao, F.-Y. Wang, and Q. Dong, “Robust optimal parallel tracking control based on adaptive dynamic programming,” IEEE Trans. Cybern., vol. 54, no. 7, pp. 4308–4321, Jul. 2024. doi: 10.1109/TCYB.2023.3312543
[29]	Q. Wei and T. Li, “Constrained-cost adaptive dynamic programming for optimal control of discrete-time nonlinear systems,” IEEE Trans. Neural Netw. Learn. Syst., vol. 35, no. 3, pp. 3251–3264, Mar. 2024. doi: 10.1109/TNNLS.2023.3237586
[30]	R. Song, G. Yang, and F. L. Lewis, “Nearly optimal control for mixed zero-sum game based on off-policy integral reinforcement learning,” IEEE Trans. Neural Netw. Learn. Syst., vol. 35, no. 2, pp. 2793–2804, Feb. 2024. doi: 10.1109/TNNLS.2022.3191847
[31]	D. P. Bertsekas, “Value and policy iterations in optimal control and adaptive dynamic programming,” IEEE Trans. Neural Netw. Learn. Syst., vol. 28, no. 3, pp. 500–509, Mar. 2017. doi: 10.1109/TNNLS.2015.2503980
[32]	Q. Wei, F. L. Lewis, D. Liu, R. Song, and H. Lin, “Discrete-time local value iteration adaptive dynamic programming: Convergence analysis,” IEEE Trans. Syst., Man, Cybern., Syst., vol. 48, no. 6, pp. 875–891, Jun. 2018. doi: 10.1109/TSMC.2016.2623766
[33]	Q. Wei, D. Liu, and H. Lin, “Value iteration adaptive dynamic programming for optimal control of discrete-time nonlinear systems,” IEEE Trans. Cybern., vol. 46, no. 3, pp. 840–853, Mar. 2016. doi: 10.1109/TCYB.2015.2492242
[34]	Q. Wei, H. Li, X. Yang, and H. He, “Continuous-time distributed policy iteration for multicontroller nonlinear systems,” IEEE Trans. Cybern., vol. 51, no. 5, pp. 2372–2383, May 2021. doi: 10.1109/TCYB.2020.2979614
[35]	Q. Wei, D. Liu, Q. Lin, and R. Song, “Discrete-time optimal control via local policy iteration adaptive dynamic programming,” IEEE Trans. Cybern., vol. 47, no. 10, pp. 3367–3379, Oct. 2017. doi: 10.1109/TCYB.2016.2586082
[36]	R. Song, L. Liu, L. Xia, and F. L. Lewis, “Online optimal event-triggered H∞ control for nonlinear systems with constrained state and input,” IEEE Trans. Syst., Man, Cybern., Syst., vol. 53, no. 1, pp. 131–141, Jan. 2023. doi: 10.1109/TSMC.2022.3173275
[37]	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, Mar. 2023. doi: 10.1109/JAS.2022.106070
[38]	R. R. Nair, L. Behera, and S. Kumar, “Event-triggered finite-time integral sliding mode controller for consensus-based formation of multirobot systems with disturbances,” IEEE Trans. Control Syst. Technol., vol. 27, no. 1, pp. 39–47, Jan. 2019.
[39]	W. Zhao, W. Yu, and H. Zhang, “Event-triggered optimal consensus tracking control for multi-agent systems with unknown internal states and disturbances,” Nonlinear Analysis: Hybrid Systems, vol. 33, pp. 227–248, Aug. 2019. doi: 10.1016/j.nahs.2019.03.003
[40]	M. Tan, Z. Liu, C. L. P. Chen, Y. Zhang, and Z. Wu, “Optimized adaptive consensus tracking control for uncertain nonlinear multiagent systems using a new event-triggered communication mechanism,” Inf. Sci., vol. 605, pp. 301–316, Aug. 2022. doi: 10.1016/j.ins.2022.05.030
[41]	J. Lu, Q. Wei, Z. Wang, T. Zhou, and F.-Y. Wang, “Event-triggered optimal control for discrete-time multi-player non-zero-sum games using parallel control,” Inf. Sci., vol. 584, pp. 519–535, Jan. 2022. doi: 10.1016/j.ins.2021.10.073
[42]	J. Lu, Q. Wei, T. Zhou, Z. Wang, and F.-Y. Wang, “Event-triggered near-optimal control for unknown discrete-time nonlinear systems using parallel control,” IEEE Trans. Cybern., vol. 53, no. 3, pp. 1890–1904, Mar. 2023. doi: 10.1109/TCYB.2022.3164977
[43]	P. Petersen, M. Raslan, and F. Voigtlaender, “Topological properties of the set of functions generated by neural networks of fixed size,” Found. Comput. Math., vol. 21, no. 2, pp. 375–444, May 2021. doi: 10.1007/s10208-020-09461-0
[44]	G. Wen, C. L. P. Chen, and S. S. Ge, “Simplified optimized backstepping control for a class of nonlinear strict-feedback systems with unknown dynamic functions,” IEEE Trans. Cybern., vol. 51, no. 9, pp. 4567–4580, Sept. 2021. doi: 10.1109/TCYB.2020.3002108
[45]	F. Lin, Robust Control Design: An Optimal Control Approach. Newy York: John Wiley/RSP, 2007.
[46]	B. Li, S. Wen, Z. Yan, G. Wen, and T. Huang, “A survey on the control lyapunov function and control barrier function for nonlinear-affine control systems,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 3, pp. 584–602, Mar. 2023. doi: 10.1109/JAS.2023.123075
[47]	K. G. Vamvoudakis and F. L. Lewis, “Online actor-critic algorithm to solve the continuous-time infinite horizon optimal control problem,” Automatica (Oxford), vol. 46, no. 5, pp. 878–888, May 2010. doi: 10.1016/j.automatica.2010.02.018
[48]	R. W. Beard, G. N. Saridis, and J. T. Wen, “Galerkin approximations of the generalized Hamilton-Jacobi-Bellman equation,” Automatica, vol. 33, no. 12, pp. 2159–2177, Dec. 1997. doi: 10.1016/S0005-1098(97)00128-3
[49]	H. K. Khalil, Nonlinear Systems, ser. 3rd ed, New Jersey: Prentice Hall, 2002.
[50]	J. Wang, Z. Zhang, B. Tian, and Q. Zong, “Event-based robust optimal consensus control for nonlinear multiagent system with local adaptive dynamic programming,” IEEE Trans. Neural Netw. Learn. Syst., vol. 35, no. 1, pp. 1073–1086, Jan. 2024. doi: 10.1109/TNNLS.2022.3180054

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Event-Triggered Robust Parallel Optimal Consensus Control for Multiagent Systems

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