Sliding Model Control with Upper Bound Adaptive Learning for Virtual Axis Machine Tool

Abstract

Virtual axis machine tool is widely used in the machining process of complicated curved surface, pointing on the uncertain factors that exists in the virtual axis machine tool system will affect the process accuracy of the virtual axis machine tool, also take the problem that the upper bound of the interference of the actual system are unable to be measured into considering, in this paper, a sliding model control scheme with upper bound adaptive learning based on RBF networks, and the proposed scheme is realized in the MATLAB platform. The simulation results revealed that compared with the traditional sliding model control, the proposed control algorithm has the good performance on position tracking, the error upper bound prediction, chattering reducing, fasting convergence and so forth.