Advanced Search
Volume 41 Issue 12
Dec.  2015
Turn off MathJax
Article Contents
Journal of Beijing University of Aeronautics and Astronautics  > 2015  >  41(12): 2384-2390.
SUN Jipeng, MENG Deyuan, DU Mingjun, et al. Distributed adaptive iterative learning control for multiple robot manipulators[J]. Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(12): 2384-2390. doi: 10.13700/j.bh.1001-5965.2014.0831(in Chinese)
Citation: SUN Jipeng, MENG Deyuan, DU Mingjun, et al. Distributed adaptive iterative learning control for multiple robot manipulators[J]. Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(12): 2384-2390. doi: 10.13700/j.bh.1001-5965.2014.0831(in Chinese)
shu

Distributed adaptive iterative learning control for multiple robot manipulators

doi: 10.13700/j.bh.1001-5965.2014.0831
  • 1.

    The Seventh Research Division, Beijing University of Aeronautics and Astronautics, Beijing 100191, China

  • 2. School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China

  • Received Date: 29 Dec 2014
  • Rev Recd Date: 03 Apr 2015
  • Publish Date: 20 Dec 2015
    Abstract
  • A hybrid adaptive and iterative learning method was proposed to obtain distributed control protocols for multiple manipulator systems with undirected interaction topology to achieve consensus tracking of the specified desired reference trajectory. By introducing an appropriate adaptive iterative learning parameter, the proposed adaptive iterative learning control (AILC) protocol can overcome the effects of disturbances and model uncertainties of manipulators, where the AILC law of each manipulator needs only the relative information between it and its nearest neighbors. Moreover, it is shown that all manipulators can be rendered to achieve the perfect tracking of the desired reference trajectory though its information can be accessed by only a portion of manipulators, where the boundedness of both the tracking error and the control input can be simultaneously guaranteed. In addition, the Lyapunov analysis method is employed to validate the obtained results, and the effectiveness of the proposed AILC protocol is illustrated through an example.

     

    • FullText(HTML)
  • loading
    • References(17)
  • [1]
    吴振彪.工业机器人[M].武汉:华中理工出版社,2004:6-10. Wu Z B.Industrial robot[M].Wuhan:Huazhong Institute of Technology Press,2004:6-10(in Chinese).
    [2]
    Ren W,Beard R W.Consensus seeking in multi-agent systems under dynamically changing interaction topologies[J].IEEE Transactions on Automatic Control,2005,50(5):655-661.
    [3]
    Olfati-Saber R,Murray R M.Consensus problems in networks of agents with switching topology and time-delays[J].IEEE Transactions on Automatic Control,2004,49(9):1520-1533.
    [4]
    Olfati-Saber R.Ultrafast consensus in small-world networks[C]//Proceedings of the 2005 American Control Conference.Piscataway,NJ:IEEE Press,2005,4:2371-2378.
    [5]
    Hong Y G,Hu J P,Gao L X.Tracking control for multi-agent consensus with an active leader and variable topology[J].Automatica,2006,42(7):1177-1182.
    [6]
    Hu J P,Hong Y G.Leader-following coordi-nation of multi-agent systems with coupling time delays[J].Physica A:Statistical Mechanics and Its Applications,2007,374(2):853-863.
    [7]
    Ni W,Cheng D Z.Leader-following consensus of multi-agent systems under fixed and switching topologies[J].Systems & Control Letters,2010,59(3-4):209-217.
    [8]
    Zhang H W,Frank L.Optimal design for synchronization of cooperative systems:State feedback,observer and output feedback[J].IEEE Transactions on Automatic Control,2011,56(8):1948-1952.
    [9]
    Ren W.Multi-vehicle consensus with a time-varying reference state[J].Systems & Control Letters,2007,56(7-8):474-483.
    [10]
    Liu S,Xie L H,Lewis F L.Synchronization of multi-agent systems with delayed control input information from neighbors[J].Automatica,2011,47(10):2152-2164.
    [11]
    Li J,Li J.Adaptive iterative learning control for consensus of multi-agent systems[J].IET Control Theory and Applications,2013,7(1):136-142.
    [12]
    Meng D Y,Jia Y M.Iterative learning approaches to design finite-time consensus protocols for multi-agent systems[J].Systems & Control Letters,2012,61(1):187-194.
    [13]
    Su J B.Base calibration for dual robot system[J].Control Theory & Applications,1998,15(4):575-582.
    [14]
    Kosuge K,Ishikawa J,Furuta K,et al.Control of single master multi-slave manipulator system using VIM[C]//Proceeding of IEEE International Conference on Robotics and Automation.Piscataway,NJ:IEEE Press,1990:1172-1177.
    [15]
    Tayebi A,Islam S.Adaptive iterative learning control for robot manipulators: Experimental results[J].Control Engineering Practice,2006,14(7):843-851.
    [16]
    Chien C J,Tayebi A.Further results on adaptive iterative learning control of robot manipulators[J].Automatica,2008,44(3):830-837.
    [17]
    孙继鹏,孟德元.多机器人手臂系统一致性的自适应迭代学习控制[C]//第十届中国智能系统会议(CISC'14).北京:智能空天系统专业委员会,2014:346-351. Sun J P,Meng D Y.Adaptive iterative learning control for consensus tracking of multi-manipulator systems[C]//The 10th Chinese Intelligent System Conference(CISC'14).Beijing:Society of Intelligent Aerospace Systems,2014:346-351(in Chinese).
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Get Citation
    PDF
    XML

    Article Metrics

    Article views(958) PDF downloads(436) Cited by()
    Proportional views
    Related

    Copyright © Journal of Beijing University of Aeronautics and Astronautics

    Address: Editorial Department of Journal of Beijing University of Aeronautics and Astronautics, 37 Xueyuan Road, Haidian District, Beijing Post Code: 100191 Email: jbuaa@cqjj8.com

    Supported by: Beijing Renhe Information Technology Co., Ltd.    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return