Robust neural network control of rigid-link electrically-driven robots

Abstract

A robust neural network (NN) controller is proposed for the motion control of rigid-link electrically-driven (RLED) robots. The NNs are used to approximate two very complicated nonlinear functions. The main advantage of our approach is that the NN weights are tuned online, with no off-line learning phase required. Most importantly, we can guarantee the uniformly, ultimately bounded (UUB) stability of tracking errors and NN weights. When compared with standard adaptive robot controllers, we do not require persistent excitation conditions and no lengthy and tedious preliminary analysis to determine a regression matrix is needed.