Recurrent Online and Transfer Learning of a CNC-Machining Center with Support Vector Machines

Abstract

Data-driven learning methods represent a promising field of research to complement classical approaches in the area of control theory. Within the German cluster of excellence “Internet of Production” (IoP), model-based control strategies are researched using collective knowledge accumulated in shared databases, and adapted online according to sensor acquired data. With their inherent generalization ability and affinity for greybox modeling, Support Vector Machines (SVM) are very suitable for such online identification and adaption. However, the computational efficiency of the identification, while maintaining accuracy, is crucial for the real-time capability of the overall framework. This work compares different definitions of the learning problem with SVM for the identification of dynamic systems. Computational efficiency within the given framework is thereby of particular interest. In addition, an extension of existing libraries by transfer learning capabilities is investigated to further speed up the recurrent online identification scheme. The results suggest that SVM with “Sequential Minimal Optimization” (SMO) qualify as a real-time capable general purpose identification approach for model-based control of the derived framework. The addition of transfer learning heavily contributes to the real-time capability.