Research on RBF neural network adaptive control of three-point contactless measuring device for CNC roller grinder

Abstract

Abstract In order to improve the CNC roll grinder production efficiency, a novel three-point non-contact measurement device is proposed. An adaptive back-stepping control system that combines with a radial basis function neural network (RBFNN) was designed to control the measurement device working position and also to track the periodic reference movement trajectory. In the proposed control system, the RBFNN approximation and tracking ability are used to identify the unknown measuring device dynamic information. Then, the Lyapunov stability theorem is used to derive the adaptive online learning algorithm. All control algorithms are placed in the control chip based on the TMS320F28335 archive. The simulation results show that the proposed control system has a good control effect when applied to the three-point non-contact measuring device in CNC roll grinders.