Sequential design for model calibration in iterative learning control of DC motor

The positioning problem for repeated DC motor runs based on the iterative learning control technique enhanced with model calibration is discussed. In order to increase the quality of control and reduce the model uncertainty, the conventional iterative control approach is enhanced with parameter estimation of the mathematical model. This is achieved through proper adaptation of the iterative experimental design technique properly incorporated into general iterative control scheme. The setting examined here correspond to situation where from among all the measurements gathered in repeated trials of the process the most informative observations are selected in order to provide an update of the parameter estimates. In such a way, in each iteration loop both the quality of control and model of the process can be significantly improved. A proposed approach is verified on the application example of DC servo motor system.