ROBUST NEURAL NETWORK CONTROL OF RIGID LINK FLEXIBLE‐JOINT ROBOTS

Abstract

A robust Neural Network (NN) controller is proposed for the motion control of rigid‐link flexible‐joint (RLFJ) robots. No weak joint elasticity assumption is needed. The NNs are used to approximate three very complicated nonlinear functions. Our NN approach requires no off‐line learning phase, and no lengthy and tedious preliminary analysis to find the regression matrices. Most importantly, we can guarantee the uniformly ultimately bounded (UUB) stability of tracking errors and NN weights. The controller can be regarded as a universal reusable controller because the same controller can be directly applied to different RLFJ robots with different masses and lengths within the same class, for instance, of two‐link revolute RLFJ robots.