Support vector regression based friction modeling and compensation in motion control system

Friction has been experimentally shown to be one of the major sources of performance degradation in motion control system. Although for model-based friction compensation, several sophisticated friction models have been proposed in the literatures, there exists no universally agreed parametric friction model, which by implication has made selection of an appropriate parametric model difficult. More so, accurate determination of the parameters of these sophisticated parametric friction models has been challenging due to complexity of friction nonlinearities. Motivated by the need for a simple, non-parametric based, and yet effective friction compensation in motion control system, an Artificial Intelligent (AI)-based (non-parametric) friction model using v-Support Vector Regression (v-SVR) is proposed in this work to estimate the non-linear friction in a motion control system. Unlike conventional SVR technique, v-SVR is characterized with fewer parameters for its development, and requires less development time. The effectiveness of the developed model in representing and compensating for the frictional effects is evaluated experimentally on a rotary experimental motion system. The performance is benchmarked with three parametric based (Coulomb, Tustin, and Lorentzian) friction models. The results show the v-SVR as a viable and efficient alternative to the parametric-based techniques in representing and compensating friction effects.