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作 者:Yufeng Lian Xingtian Xiao Jiliang Zhang Long Jin Junzhi Yu Zhongbo Sun
机构地区:[1]the Department of Control Engineering,Changchun University of Technology,Changchun 130012,China [2]IEEE [3]the College of Information Science and Engineering,Northeastern University,Shenyang 110819 [4]the National Mobile Communications Research Laboratory,Southeast University,Nanjing 210096,China [5]the School of Information Science and Engineering,Lanzhou University,Lanzhou 730000,China [6]the State Key Laboratory for Turbulence and Complex Systems,Department of Advanced Manufacturing and Robotics,College of Engineering,Peking University,Beijing 100871,China
出 处:《IEEE/CAA Journal of Automatica Sinica》2024年第7期1605-1620,共16页自动化学报(英文版)
基 金: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);the Open Research Fund of National Mobile Communications Research Laboratory,Southeast University (2024D09)。
摘 要: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.
关 键 词:Cooperative motion control noise-tolerant zeroing neural network(NTZNN) omnidirectional mobile manipulator(OMM) repetitive motion planning
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