Event-Triggered Robust Parallel Optimal Consensus Control for Multiagent Systems

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

doi:10.1109/JAS.2024.124773

IEEE/CAA Journal of Automatica Sinica

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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. 40–53, Jan. 2025. doi: 10.1109/JAS.2024.124773

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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. 40–53, Jan. 2025. doi: 10.1109/JAS.2024.124773

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Event-Triggered Robust Parallel Optimal Consensus Control for Multiagent Systems

doi: 10.1109/JAS.2024.124773

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/A2, 0015/2020/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, and 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; Shanshan Jiao, e-mail: 2202853ymi30001@student.must.edu.mo
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

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Event-Triggered Robust Parallel Optimal Consensus Control for Multiagent Systems

doi: 10.1109/JAS.2024.124773

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