A robust neural-network (NN) controller is proposed for the motion con
trol of rigid-link electrically driven (RLED) robots. Two-layer NN's a
re used to approximate two very complicated nonlinear functions. The m
ain advantage of our approach is that the NN weights are tuned on-line
, with no off-line learning phase required. Most importantly, we can g
uarantee the uniformly ultimately bounded (WB) stability of tracking e
rrors and NN weights. When compared with standard adaptive robot contr
ollers, we do not require lengthy and tedious preliminary analysis to
determine a regression matrix, The controller can be regarded as a uni
versal reusable controller because the same controller can be applied
to any type of RLED robots without any modifications.