Unified Output Feedback Based Prescribed Performance Consensus Tracking Control of Heterogeneous Multi-Agent Systems

Dahui Luo; Yujuan Wang; Frank L. Lewis; Yongduan Song

doi:10.1109/JAS.2024.125094

IEEE/CAA Journal of Automatica Sinica

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D. Luo, Y. Wang, F. Lewis, and Y. Song, “Unified output feedback based prescribed performance consensus tracking control of heterogeneous multi-agent systems,” IEEE/CAA J. Autom. Sinica, 2025. doi: 10.1109/JAS.2024.125094

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D. Luo, Y. Wang, F. Lewis, and Y. Song, “Unified output feedback based prescribed performance consensus tracking control of heterogeneous multi-agent systems,” IEEE/CAA J. Autom. Sinica, 2025. doi: 10.1109/JAS.2024.125094

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Unified Output Feedback Based Prescribed Performance Consensus Tracking Control of Heterogeneous Multi-Agent Systems

doi: 10.1109/JAS.2024.125094

Funds: This work was supported in part by the National Key Research and Development Program of China (2023YFA1011803), the National Natural Science Foundation of China (62273064, W2411061), the Chongqing Natural Science Foundation (CSTB2023NSCQ-MSX0588), the Innovation Support Program for International Students Returning to China (cx2022016), and the Central University Project (2023CDJKYJH047)

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Author Bio:
Dahui Luo received her Ph.D. degree in control science and engineering from Chongqing University, China, in 2024. She is currently a lecturer with the school of mathematics and statistics in Guizhou University. Her research interests include cooperative control, adaptive control, prescribed-time control

Yujuan Wang received her Ph.D. degree in control theory and control engineering from Chongqing University, China, in 2016. She is currently a Professor with the School of Automation in Chongqing University. She was a Research Associate with the Department of Electrical and Electronic Engineering in the University of Hong Kong during 2017−2018. Her research interests include cooperative control, adaptive control, finite-time control, fault-tolerant control

Frank L. Lewis received the bachelor degree in physics/EE and the M.S.E.E. degree from Rice University, the M.S. degree in aeronautical engineering from the University of Florida, and the Ph.D. degree from Georgia Tech. He is a UTA Charter Distinguished Scholar Professor, a UTA Distinguished Teaching Professor, and the Moncrief-O¡¯Donnell Chair at the University of Texas at Arlington Research Institute. He ranked at position 88 worldwide, 66 in the USA, and three in Texas of all scientists in computer science and electronics, by Guide2Research.com. He has 80000 Google Scholar Citations. He works in feedback control, intelligent systems, reinforcement learning, cooperative control systems, and nonlinear systems. He is the author of seven U.S. patents, numerous journal special issues, 445 journal articles, 20 books, including the textbooks Optimal Control, Aircraft Control, Optimal Estimation, and Robot Manipulator Control. He is a Fellow of the National Academy of Inventors, IEEE, IFAC, AAAS, the U.K. Institute of Measurement and Control, and PE Texas; and U.K. Chartered Engineer. He received the Fulbright Research Award, the NSF Research Initiation Grant, the ASEE Terman Award, the International Neural Network Society Gabor Award, the U.K. Inst Measurement and Control Honeywell Field Engineering Medal, the IEEE Computational Intelligence Society Neural Networks Pioneer Award, the AIAA Intelligent Systems Award, and the AACC Ragazzini Award

Yongduan Song received the Ph.D. degree in electrical and computer engineering from Tennessee Technological University, Cookeville, TN, USA, in 1992. He held a tenured Full Professor with North Carolina A&T State University, Greensboro, NC, USA, from 1993 to 2008 and a Langley Distinguished Professor with the National Institute of Aerospace, Hampton, VA, USA, from 2005 to 2008. He is currently the Dean of the School of Automation, Chongqing University, Chongqing, China. He was one of the six Langley Distinguished Professors with the National Institute of Aerospace (NIA), Hampton, VA, USA, and the Founding Director of Cooperative Systems with NIA. His current research interests include intelligent systems, guidance navigation and control, bio-inspired adaptive and cooperative systems. Prof. Song was a recipient of several competitive research awards from the National Science Foundation, the National Aeronautics and Space Administration, the U.S. Air Force Office, the U.S. Army Research Office, and the U.S. Naval Research Office. He is an IEEE Fellow and has served/been serving as an Associate Editor for several prestigious international journals, including the IEEE Transactions on Automatic Control, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Systems, Man and Cybernetics, etc. He is the Editor-in-Chief for IEEE Transactions on Neural Networks and Learning Systems
Corresponding author: Yujuan Wang, e-mail: yjwang66@cqu.edu.cn
Received Date: 2024-10-29
Accepted Date: 2024-12-03

Available Online: 2025-03-18

Abstract

Abstract

This paper proposes an output-feedback based prescribed performance consensus tracking control methodology for a class of heterogeneous multi-agent systems (HMASs) with inconsistent system structure, where the performance behavior is allowed to be different from that of each other. Both the heterogeneous system structures and the nonidentical performance requirements make the control problem much more challenging than that of MASs with identical structure and performance requirement. This is mainly due to the coupling effect of the system dynamics and performance restriction of each agent in the cooperative control action. The key to solve this problem is to introduce a dual-phase performance-guaranteed method, in which the consensus tracking error is decomposed into auxiliary tracking error and filter tracking error and then the whole performance control is decomposed into two phases. By confining the two errors respectively, the practical tracking error can be proved to be explicitly confined within an arbitrarily given performance envelope by merely adjusting the design parameters rather than modifying control structure. Moreover, the prescribed performance control (PPC) result is not only uniform with any initial conditions and design parameters, allowing it to be global, but also unifying both the global and semi-global result into one frame, distinguishing itself from most existing PPC works where either only global or only semi-global result is guaranteed. Finally, the effectiveness of the proposed control scheme is confirmed by the simulation conducted on a group of tunnel-diode circuits (TDC).
- Auxiliary filter,
- heterogeneous multi-agent systems (HMASs),
- output-feedback,
- prescribed performance

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References

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Unified Output Feedback Based Prescribed Performance Consensus Tracking Control of Heterogeneous Multi-Agent Systems

doi: 10.1109/JAS.2024.125094

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