Neural Dynamics for Cooperative Motion Control of Omnidirectional Mobile Manipulators in the Presence of Noises: A Distributed Approach

Yufeng Lian; Xingtian Xiao; Jiliang Zhang; Long Jin; Junzhi Yu; Zhongbo Sun

IEEE/CAA Journal of Automatica Sinica

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

Y. Lian, X. Xiao, J. Zhang, L. Jin, J. Yu, and Z. Sun, “Neural dynamics for cooperative motion control of omnidirectional mobile manipulators in the presence of noises: A distributed approach,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 7, pp. 1–16, Jul. 2024.

Citation:

Y. Lian, X. Xiao, J. Zhang, L. Jin, J. Yu, and Z. Sun, “Neural dynamics for cooperative motion control of omnidirectional mobile manipulators in the presence of noises: A distributed approach,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 7, pp. 1–16, Jul. 2024.

Citation:

Y. Lian, X. Xiao, J. Zhang, L. Jin, J. Yu, and Z. Sun, “Neural dynamics for cooperative motion control of omnidirectional mobile manipulators in the presence of noises: A distributed approach,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 7, pp. 1–16, Jul. 2024.

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Neural Dynamics for Cooperative Motion Control of Omnidirectional Mobile Manipulators in the Presence of Noises: A Distributed Approach

Funds: The work was supported in part by the National Natural Science Foundation of China (62373065, 61873304, 62173048, 62106023), the Innovation and Entrepreneurship Talent funding Project of Jilin Province (2022QN04), the Changchun Science and Technology Project (21ZY41), and the Open Research Fund of National Mobile Communications Research Laboratory, Southeast University (2024D09)

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Author Bio:
Yufeng Lian received the B.S. degree in automation and the M.S. degrees in control theory and control engineering from Changchun University of Technology in 2005 and 2008, respectively. He received the Ph.D. degree in control theory and control engineering with Jilin University in 2015. He is currently a lecturer at Changchun University of Technology. His research interests include the modeling, optimization and the control of complex systems, vehicle dynamics and control, and active safety control systems for electric vehicles

Xingtian Xiao received the B.E. degree in electrical engineering and automation from Qilu University of Technology in 2020. He is currently pursuing the master degree at the School of Electrical and Electronic Engineering, Changchun University of Technology. His research interests include mobile robotic arms, neural networks, modeling, optimization, and cooperative control

Jiliang Zhang (Senior Member, IEEE) received the B.E., M.E., and Ph.D. degrees from Harbin Institute of Technology, in 2007, 2009, and 2014, respectively. He was an Associate Professor with the School of Information Science and Engineering, Lanzhou University from 2017 to 2019, a Researcher in the Department of Electrical Engineering, Chalmers University of Technology, Sweden from 2017 to 2018, a Marie Curie Research Fellow and a KTP Associate in the Department of Electronic and Electrical Engineering, The University of Sheffield, UK from 2018 to 2022. He is currently a Full Professor at the College of Information Science and Engineering, Northeastern University. His research interests include, but are not limited to wireless channel modeling, modulation system, relay system, vehicular communications, ultra-dense small cell networks, and smart environment modeling. He has pioneered systematic building wireless performance evaluation, modeling, and optimization, with the key concepts summarized in Fundamental Wireless Performance of a Building, IEEE Wireless Communications, 29(1), 2022. He is also the Academic Editor for Wireless Communications and Mobile Computing since 2019. He was the recipient of the IEEE Wireless Communications Letters Exemplary Reviewer Award in 2021

Long Jin (Senior Member, IEEE) received the B.E. degree in automation and the Ph.D. degree in information and communication engineering from Sun Yat-sen University in 2011 and 2016, respectively. He received a postdoctoral training with the Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China, from 2016 to 2017. In 2017, he joined the School of Information Science and Engineering, Lanzhou University, as a Professor of computer science and engineering. His current research interests include neural networks, robotics, optimization, and intelligent computing. Prof. Jin is currently serving as an Associate Editor for the Neural Processing Letters

Junzhi Yu (Fellow, IEEE) received the B.E. degree in safety engineering and the M.E. degree in precision instruments and mechanology from the North University of China in 1998, and 2001, respectively, and the Ph.D. degree in control theory and control engineering from the Institute of Automation, Chinese Academy of Sciences in 2003. From 2004 to 2006, he was a Postdoctoral Research Fellow with the Center for Systems and Control, Peking University. In 2006, he was an Associate Professor with the Institute of Automation, Chinese Academy of Sciences, where he became a Full Professor, in 2012. In 2018, he joined the College of Engineering, Peking University, as a Tenured Full Professor. His research interests include intelligent robots, motion control, and intelligent mechatronic systems

Zhongbo Sun received the B.S. degree in mathematics and applied mathematics from Changchun University, the M.S. degree in operational research and control theory from Guilin University of Electronic Technology, and the Ph.D. degree in control theory and control engineering from Jilin University, in 2007, 2010, and 2016, respectively. In 2020, he was a Postdoctoral Fellow with the Key Laboratory of Bionics Engineering, Ministry of Education, Jilin University. In 2019, he was also an Academic Visitor with Peking University, and in 2020, he was also an Academic Visitor with Michigan State University. He is currently a Full Professor with the Department of Control Engineering, Changchun University of Technology. His research interests include rehabilitation robots, bipedal robots, neural networks, and nonlinear programming
Corresponding author: Zhongbo Sun, e-mail: zbsun@ccut.edu.cn
Received Date: 2024-01-18
Revised Date: 2024-02-28
Accepted Date: 2024-03-18

Available Online: 2024-05-08

Abstract

Abstract

This paper presents a distributed scheme with limited communications, aiming to achieve cooperative motion control for multiple omnidirectional mobile manipulators (MOMMs). The proposed scheme extends the existing single-agent motion control to cater to scenarios involving the cooperative operation of MOMMs. Specifically, squeeze-free cooperative load transportation is achieved for the end-effectors of MOMMs by incorporating cooperative repetitive motion planning (CRMP), while guiding each individual to desired poses. Then, the distributed scheme is formulated as a time-varying quadratic programming (QP) and solved online utilizing a noise-tolerant zeroing neural network (NTZNN). Theoretical analysis shows that the NTZNN model converges globally to the optimal solution of QP in the presence of noise. Finally, the effectiveness of the control design is demonstrated by numerical simulations and physical platform experiments.

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References

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Neural Dynamics for Cooperative Motion Control of Omnidirectional Mobile Manipulators in the Presence of Noises: A Distributed Approach

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