Embedded Iterative Learning Contouring Controller Based on Precise Estimation of Contour Error for CNC Machine Tools

In machine tool application, a machined shape of workpiece should be considered in machine tool control systems. The contour error relates to it directly. Although most existing contouring controllers are based on feedback control, this paper proposes an embedded iterative learning contouring controller (EILCC) by considering actual contour error (ACE) which is the minimum distance between actual position and desired trajectory. The proposed method modifies original trajectory by ACE compensation with a PID manner, iteratively. The proposed controller can be directly applied to existing commercial machines without any change of their original controllers. The proposed method has been verified by simulation and experiment in commercial CNC machine tool with a right-angled sharp-corner trajectory which normally produces a large contour error around the corner due to unsmoothness. Comparison with a previous EILCC without ACE was done to evaluate its performance. Experimental results have shown that the maximum contour error was reduced by 78.8 % and 9.7 % as compared to the typical feedback controller (FBC) and EILCC without ACE (with estimated contour error), respectively.