Active vibration suppression in robotic milling using optimal control

Abstract

Six degree-of-freedom (6-dof) industrial robots are attractive alternatives to Computer Numerical Control (CNC) machine tools for milling of large parts because of their low-cost, greater versatility, and larger work volume. However, 6-dof industrial robots are significantly more compliant than CNC machine tools, which makes them prone to vibrations during milling. An additional complexity of industrial robots is their pose-dependent vibration characteristics. This paper presents a pose-dependent optimal control methodology to actively suppress tool tip vibrations generated by the periodic milling forces in robotic milling. Discretely sampled robot structural modal parameters as a function of robot configuration (pose) are used to develop a data-driven Gaussian Process Regression (GPR) model. The model is then utilized to solve the Linear Quadratic Regulator (LQR) optimal control problem to obtain pose-dependent controller gains necessary for vibration suppression. The pose-dependent controller is implemented on a 6-dof industrial robot and its performance evaluated through process-independent offset mass experiments and through milling experiments. The methodology is shown to be effective in decreasing the tool tip vibrations and improving the machining accuracy in robotic milling.