Iterative Learning for Machine Tools Using a Convex Optimisation Approach

Abstract

Dynamic, quasi-static and motion control deviations lead to nonlinear but systematic tracking errors. It is shown that these errors can be reduced significantly by adjusting the set points using an optimization based iterative learning approach. This method uses either values obtained from internal encoders or alternatively tool center point measurements. The approach is presented, discussed and validated using simulation and measurement results.