Mixed-reality environment for frictional parameters identification in servo-pneumatic system

This paper outlines a method to identify the friction parameters for servo-pneumatic systems using a mixed-reality environment. To acquire system friction parameters accurately can be extremely difficult once the servo-system has been assembled because of its highly nonlinear nature, which causes a great difficulty in servo-pneumatic system modelling and control. In this research, a mixed-reality environment has been employed to determine the friction parameters effectively and efficiently through online identification. Traditionally, friction parameters identification can be performed manually or automatically using traditional optimization methods or modern ones such as neural networks. The advantages of the proposed method are the high accuracy in the estimated parameters, its simplicity and its speed. An experimental case study has been conducted and the results showed the accuracy and effectiveness of the proposed method.