Modeling and identification of the multi-axis drive system using support vector machine and granular computing

Advanced manufacturing technology requires high-precision capability in multi-axis computer numerical control (CNC) machine tools. At present, the modeling and identification for the drive system of CNC machine tools has some defects. In order to solve the problem, some interdisciplinary theories and methods, such as support vector machines, granular computing, artificial immune algorithms, and particle swarm optimization algorithms, have been used to model and identify multi-axis drive systems for CNC machine tools. An identification method using a support vector machine, based on granular computing, is presented to identify a multi-axis servo drive system model for improving the precision of model identification, and an immune particle swarm optimization algorithm, based on crossover and mutation functions, is proposed to optimize the structure parameters of the support vector machine based on granular computing. The proposed identification method was evaluated by experiments using the multi-axis servo drive system. The experimental results showed that the proposed approach is capable of improving modeling and identification precision.