Practical Nonlinear Model Predictive Control of a CNC Machining Center with Support Vector Machines

IEEE/ASME International Conference on Advanced Intelligent Mechatronics, pp. 1625–1631

Abstract

The productivity of CNC machining and milling in specific is limited by the endurable forces of the working tools. An increased feed velocity also induces higher active forces on the tool, such that the feed velocity has to be maximized within dynamical limits for an optimal operation of the CNC machining center. Model predictive control (MPC) strategies, together with an according target selector enable this kind of optimized operation. The optimization is possible due to models, which describe the feed dynamics and the relationship between the feed and maximum force. However in such a scenario, an accurate modeling and control of the feed dynamics become more crucial, as the built-in routines within the machining center induce unknown nonlinearities. Thus, to achieve the aforementioned objectives, the authors propose a practical nonlinear model predictive control (PNMPC) strategy for milling, incorporating Support Vector Machines (SVM) for both, the target selector and the identification of the unknown nonlinearities. The results show an improved overall control performance with PNMPC, compared to a linear time-varying MPC (LTV-MPC) with a successively linearized model.

Authors 6

1. Muzaffer Ay Aachen Institute of Automatic Control (IRT)

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Institute of Automatic Control,Aachen,Germany

   Institute of Automatic Control, RWTH Aachen University, Aachen, Germany

2. Max Schwenzer Aachen Laboratory for Machine Tools and Production Engineering (WZL)

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Laboratory for Machine Tools and Production Engineering,Aachen,Germany

   Laboratory for Machine Tools and Production Engineering, RWTH Aachen University, Aachen, Germany

3. Sebastian Stemmler Aachen Institute of Automatic Control (IRT)

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Institute of Automatic Control,Aachen,Germany

   Institute of Automatic Control, RWTH Aachen University, Aachen, Germany

4. Adrian Karl Rüppel Aachen Laboratory for Machine Tools and Production Engineering (WZL)

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Laboratory for Machine Tools and Production Engineering,Aachen,Germany

   Laboratory for Machine Tools and Production Engineering, RWTH Aachen University, Aachen, Germany

5. Dirk Abel Aachen Institute of Automatic Control (IRT)

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Institute of Automatic Control,Aachen,Germany

   Institute of Automatic Control, RWTH Aachen University, Aachen, Germany

6. Thomas Bergs Aachen Laboratory for Machine Tools and Production Engineering (WZL)

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Laboratory for Machine Tools and Production Engineering,Aachen,Germany

   Laboratory for Machine Tools and Production Engineering, RWTH Aachen University, Aachen, Germany