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