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Front. Inform. Technol. Electron. Eng.  2018, Vol. 19 Issue (11): 1316-1327    
    
Adaptive robust neural control of a two-manipulator system holding a rigid object with inaccurate base frame parameters
Fan XU, Jin WANG, Guo-dong LU
State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China
Adaptive robust neural control of a two-manipulator system holding a rigid object with inaccurate base frame parameters
Fan XU, Jin WANG, Guo-dong LU
State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China
 全文: PDF 
摘要： The problem of self-tuning control with a two-manipulator system holding a rigid object in the presence of inaccurate
translational base frame parameters is addressed. An adaptive robust neural controller is proposed to cope with inaccurate trans-
lational  base  frame  parameters,  internal  force,  modeling uncertainties,  joint  friction,  and  external  disturbances.  A  radial  basis
function  neural  network  is  adopted  for  all  kinds  of  dynamical  estimation,  including  undesired  internal  force.  To  validate  the
effectiveness of the proposed approach, together with simulation studies and analysis, the position tracking errors are shown to
asymptotically converge to zero, and the internal force can be maintained in a steady range. Using an adaptive engine, this ap-
proach permits accurate online calibration of the relative translational base frame parameters of the involved manipulators. Spe-
cialized robust compensation is established for global stability. Using a Lyapunov approach, the controller is proved robust in the
face of inaccurate base frame parameters and the aforementioned uncertainties.
关键词： Cooperative manipulators;  Neural networks;  Inaccurate translational base frame;  Adaptive control;  Robust control     
Abstract: The problem of self-tuning control with a two-manipulator system holding a rigid object in the presence of inaccurate
translational base frame parameters is addressed. An adaptive robust neural controller is proposed to cope with inaccurate trans-
lational  base  frame  parameters,  internal  force,  modeling uncertainties,  joint  friction,  and  external  disturbances.  A  radial  basis
function  neural  network  is  adopted  for  all  kinds  of  dynamical  estimation,  including  undesired  internal  force.  To  validate  the
effectiveness of the proposed approach, together with simulation studies and analysis, the position tracking errors are shown to
asymptotically converge to zero, and the internal force can be maintained in a steady range. Using an adaptive engine, this ap-
proach permits accurate online calibration of the relative translational base frame parameters of the involved manipulators. Spe-
cialized robust compensation is established for global stability. Using a Lyapunov approach, the controller is proved robust in the
face of inaccurate base frame parameters and the aforementioned uncertainties.
Key words: Cooperative manipulators    Neural networks    Inaccurate translational base frame    Adaptive control    Robust control 
收稿日期: 2016-11-16 出版日期: 2019-06-13
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Fan XU
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引用本文:

Fan XU, Jin WANG, Guo-dong LU . Adaptive robust neural control of a two-manipulator system holding a rigid object with inaccurate base frame parameters. Front. Inform. Technol. Electron. Eng., 2018, 19(11): 1316-1327.

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http://www.zjujournals.com/xueshu/fitee/CN/    或    http://www.zjujournals.com/xueshu/fitee/CN/Y2018/V19/I11/1316

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