Abstract
Abstract
A robust friction compensation scheme is studied using a dual-friction-parameter observer and a recurrent neural network combined with sliding mode control. To handle non-linear friction parameters, a dual-friction-model-based observer is used to estimate the friction parameter of the LuGre friction model. In addition, an approximating method for the system uncertainty is developed using a recurrent neural network approach to improve the precision positioning degree. Simulated and experimental results are presented that validate the performance of the proposed friction compensation scheme.
Keywords
Get full access to this article
View all access options for this article.
