Optimal sensor selection for prediction-based iterative learning control of distributed parameter systems

The purpose of this study is to develop an effective computational scheme to solve the optimal tracking control problem for repeated trials in distributed parameter system where quantity under control cannot be observed directly. In such situations, the reliability of model predictions is of crucial importance as the ultimate objective in model-based control becomes the accurate forecast of the system states. Particularly, given a finite number of possible spatial locations at which sensors may reside, we select gaged sites so as to maximize the prediction accuracy. For that purpose, a suitable output criterion is proposed as a measure of the prediction quality, then the sensor selection problem is formulated in terms of optimization task and effecively solved with dedicated algorithm. The optimal measurement schedule is further incorporated into the iterative learning control scheme for effective solution of the underlying tracking control problem for the friction welding process.