Adaptive friction compensation using neural network approximations

We present a new compensation technique for a friction model, which capture s problematic friction effects such as Stribeck effects, hysteresis, stick- slip limit cycling, pre-sliding displacement and rising static friction. Th e proposed control utilizes a PD control structure and an adaptive estimate of the friction force. Specifically, a radial basis function (RBF) Is used to compensate the effects of the unknown nonlinearly occurring Stribeck pa rameter in the friction model. The main analytical result Is a stability th eorem fur the proposed compensator which can achieve regional stability of the closed-loop system. Furthermore, we show that the transient performance of the resulting adaptive system is analytically quantified. To support th e theoretical concepts, we present dynamic simulations fur the proposed con trol scheme.