Adaptive compensation of modeled friction using a RBF neural network approximation

We present a compensation technique for a friction model, which captures problematic friction effects such as Stribeck effect, hysteresis, pre-sliding displacement, stick- slip motion and stiction. The 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 non-linear friction model. The asymptotic convergence of parameter estimation errors is achieved for the system in adaptive observer form using Barbalat's Lemma. We also introduce a parameter estimation projection algorithm to avoid the parameter estimates drift when the condition of persistency of excitation is not verified. Finally, to support the theoretical concepts, we present dynamic simulations for the proposed control scheme.

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