System identification: theory for the user
System identification: theory for the user
Automatica (Journal of IFAC)
The nature of statistical learning theory
The nature of statistical learning theory
Forecasting of the daily meteorological pollution using wavelets and support vector machine
Engineering Applications of Artificial Intelligence
Friction modelling and compensation for motion control using hybrid neural network models
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Adaptive friction compensation using neural network approximations
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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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.