Data-Driven Bipartite Consensus Control for Large Workpieces Rotation of Nonlinear Multi-Robot Systems

Haoran Tan; Xueming Zhang; Yaonan Wang; You Wu; Yun Feng; Zhongsheng Hou

IEEE/CAA Journal of Automatica Sinica

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

H. Tan, X. Zhang, Y. Wang, Y. Wu, Y. Feng, and Z. Hou, “Data-driven bipartite consensus control for large workpieces rotation of nonlinear multi-robot systems,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 6, pp. 1–15, Jun. 2025.

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H. Tan, X. Zhang, Y. Wang, Y. Wu, Y. Feng, and Z. Hou, “Data-driven bipartite consensus control for large workpieces rotation of nonlinear multi-robot systems,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 6, pp. 1–15, Jun. 2025.

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Data-Driven Bipartite Consensus Control for Large Workpieces Rotation of Nonlinear Multi-Robot Systems

Funds: This work was supported in part by the National Natural Science Foundation of China (62473142, 62203161), Special Funding Support for the Construction of Innovative Provinces in Hunan Province (2021GK1010), Guangdong Basic and Applied Basic Research Foundation (2024A1515011579), and Project of State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle (72275007)

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Author Bio:
Haoran Tan (Member, IEEE) received the M.S. and Ph.D. degrees from the School of Information Science and Engineering, Central South University in 2014 and 2019, respectively. From 2019 to 2021, he was a Post-Doctoral Fellow with the College of Electrical and Information Engineering, Hunan University. He is currently an Associate Professor with the College of Electrical and Information Engineering, and the National Engineering Laboratory for Robot Visual Perception and Control Technology, Hunan University. His research interests include robotics, intelligent control, and networked multi-agent control systems

Xueming Zhang received the B.S. degree from the School of Electrical Engineering and Automation, Qilu University of Technology in 2019. From Sept. 2020 to Jun. 2023, he studied for the master degree with the Institute of System Science, School of Automation, Qingdao University, and received the M.S. degree in Jun. 2023. Now, he is pursuing the Ph.D. degree at the College of Electrical and Information Engineering, Hunan University.His research interests include model free adaptive control, point to point iterative learning control, and multi-agent systems

Yaonan Wang (Senior Member, IEEE) received the Ph.D. degree in electrical engineering from Hunan University in 1994. He was a Post-Doctoral Research Fellow with the Normal University of Defense Technology from 1994 to 1995. From 1998 to 2000, he was a Senior Humboldt Fellow in Germany, and from 2001 to 2004, a Visiting Professor with the University of Bremen, Germany. Since 1995, he has been a Professor with the College of Electrical and Information Engineering, Hunan University. His research interests include robotics and image processing. Prof. Wang is an Academician of the Chinese Academy of Engineering

You Wu received the M.S degree from Armour College of Engineering, Illinois Institute of Technology, USA, in 2012, and the Ph.D. degree from Central South University in 2019. She is currently a Post-Doctoral Fellow with the College of Electrical and Information Engineering, Hunan University, and works as a Lecturer at Changsha University of Science and Technology.Her research interests include robotics, data-driven control, and networked multi-agent control systems

Yun Feng (Member, IEEE) received the B.E. degree in automation and the M.S. degree in control theory and control engineering from the Department of Automation, Wuhan University in 2014 and 2017, respectively, and the Ph.D. degree from the Department of Systems Engineering and Engineering Management, City University of Hong Kong, Hong Kong, China, in 2020.From July to November 2019, he was a Visiting Student with the Institute for Automatic Control and Complex Systems (AKS), University of DuisburgEssen, Germany. He is currently an Associate Professor with the College of Electrical and Information Engineering and the National Engineering Laboratory for Robot Visual Perception and Control Technology, Hunan University. His research interests include distributed parameter systems, fault diagnosis, and soft robotics. He was selected for the Young Elite Scientists Sponsorship Program by the China Association for Science and Technology in 2023. He is serving as an Associate Editor for the Frankin Open

Zhongsheng Hou (Fellow, IEEE) received the bachelor and master degrees from Jilin University of Technology in 1983 and 1988, respectively, and the Ph.D. degree from Northeastern University in 1994.He was a Postdoctoral Fellow with the Harbin Institute of Technology from 1995 to 1997. And from 2002 to, 2003 he was a Visiting Scholar at Yale University, USA. In 1997, he joined Beijing Jiaotong University, where he was a Distinguished Professor and the Founding Director of Advanced Control Systems Laboratory, and the Head of the Department of Automatic Control until 2019. He is currently a Chair Professor with the School of Automation, Qingdao University. Up to now, he has over 270 peer-reviewed journal articles published and over 150 papers in prestigious conference proceedings. He has authored two monographs, Non-parametric Model and Its Adaptive Control Theory (Science Press, 1999), in Chinese, and Model Free Adaptive Control: Theory and Applications (CRC Press, 2013). His pioneering work on model-free adaptive control has been verified in more than 230 different field applications, laboratory equipment and simulations with practical background, including wide-area power systems, lateral control of autonomous vehicles, and temperature control of silicon rod. His works on data-driven learning and control has been supported by multiple projects supported by the National Natural Science Foundation of China, including three Key Projects in 2009, 2015, and 2019, and a Major International Cooperation Project in 2012. His research interests include data-driven control, model-free adaptive control, learning control, and intelligent transportation systems.Prof. Hou is a Fellow of the Chinese Association of Automation (CAA) and an IFAC Technical Committee Member of both Adaptive and Learning Systems and Transportation Systems. He is also the Founding Director of the Technical Committee on Data-Driven Control, Learning and Optimization (DDCLO) and CAA. He was a Guest Editor in two special sections on the topic of data-driven control at the IEEE Transactions on Neural Networks in 2011 and the IEEE Transactions on Industrial Electronics in 2017
Corresponding author: Xueming Zhang, e-mail: zhangxueming00@hnu.edu.cn
Received Date: 2024-07-01
Revised Date: 2024-08-12
Accepted Date: 2024-09-26

Available Online: 2025-04-28

Abstract

Abstract

In this paper, a novel data-driven bipartite consensus control scheme is proposed for the rotation problem of large workpieces with multi-robot systems (MRSs) under a directed communication topology. The rotation of a large workpiece is described as the MRSs with cooperation and antagonism interaction. By the signed graph theory, it is further transformed into a bipartite consensus control problem, where all followers are uniformly degenerated into the general nonlinear systems based on the lateral error model. To augment the flexibility of control protocol and improve control performance, a higher-dimensional full form dynamic linearization (FFDL) technique is committed to the MRSs. The control input criterion function consists of the data model based on FFDL and the bipartite consensus error based on the signed graph theory, and the proposed control protocol is given by optimizing this criterion function. In this way, this scheme has a higher degree of freedom and better adaptive adjustment capability while not excessively increasing the control method complexity, and it can also be compatible with other forms of dynamic linearization techniques in MRSs. Further, three matrix norm lemmas are introduced to deal with the challenges of stability analysis caused by higher matrix dimensions and more robots. Finally, the effectiveness of the proposed method is verified by numerical simulations.
- Bipartite consensus control,
- data-driven control (DDC),
- full form dynamic linearization (FFDL) technique,
- multi-robot systems (MRSs)

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References

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Data-Driven Bipartite Consensus Control for Large Workpieces Rotation of Nonlinear Multi-Robot Systems

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