A discrete-time inversion-based approach to iterative learning control of an overhead crane

This paper presents an inversion-based iterative learning control (ILC) for the two main axes of an overhead travelling crane that guarantees both an accurate tracking of repetitive desired trajectories for the crane load and an active damping of crane load oscillations. The main idea of the used ILC algorithm consists in a minimisation of the tracking error by iteratively improved inputs sequences using an inverse plant model. The position of the crane load is measured optically by a CMOS camera using the spatial filtering principle. Thereby, repetitive desired trajectories for the crane load position in the three-dimensional workspace can be tracked independently with high accuracy. Experimental results from an implementation on a test rig show a fast error convergence and a small remaining tracking error.