Experimental validation of constrained ILC approaches for a high speed rack feeder

Iterative learning control (ILC) is applicable to systems that are required to repeatedly track a desired trajectory of finite duration. Norm-optimal ILC can be characterised as a combined feedforward and feedback learning approach, where the tracking error from the previous trial and the tracking error of the current trial are employed to reduce the tracking error from trial to trial. In this paper, a high speed rack feeder typically used in automated warehouses is considered, which represents a flexible beam structure with a vertically moving mass. Due to kinematic constraints such as a maximum velocity and a maximum acceleration, standard ILC is not applicable if the desired trajectory violates these constraints. One possible solution would be an offline trajectory planning subject to the given kinematic constraints. This paper, however, addresses modifications of the ILC algorithm itself to cope with infeasible trajectories. Two alternative algorithms are given for this purpose and compared with each other in experiments on a test rig that replicates the dynamics of a high speed rack feeder.