Observer-Based ILC Applied to the Gantry-Tau Parallel Kinematic Robot : Modelling, Design and Experiments

Abstract

Three different approaches of iterative learning control (ILC) applied to a parallel kinematic robot are studied. First, the ILC algorithm is based on measured motor angles only. Second, tool-position estimates are used in the ILC algorithm. For evaluation, the ILC algorithm finally is based on measured tool position. Model-based tuning of the ILC filters enables learning above the resonance frequencies of the system. The approaches are compared experimentally on a Gantry-Tau prototype, with the tool performance being evaluated by using external sensors. It is concluded that the tool performance can be improved by using tool-position estimates in the ILC algorithm, compared to when using motor-angle measurements. In the paper applying ILC algorithms to a system following trajectories with so-called lead-in/lead-out is also considered, as well as dynamic modelling of the Gantry-Tau prototype.