Model and Experience-based initial input construction for iterative learning control

The initial choice of input in iterative learning control (ILC) generally has a significant effect on the error incurred over subsequent trials. In this paper techniques are developed which use experimental data gathered over previous applications of ILC in order to generate an initial input signal for the tracking of a new reference trajectory. A model-based approach is then incorporated to overcome the limitation of insufficient previous experimental data, and a robust design procedure is developed. Experimental evaluation results are obtained using a gantry robot facility