Iterative Learning Control of Repetitive Transverse Loads in Elastic Materials

This paper develops a computational scheme to solve the optimal tracking control problem by repeated trials for distributed-parameter processes where an example from elasticity analysis in structural mechanics is used as motivation. An adaptive control scheme based on iterative learning control is used to develop an effective solution of underlying optimization task. The essential feature of the resulting approach is the efficient modelling and simulation of the distributed system under consideration using discretization based on the finite element method. Also to reduce the uncertainty of the model used for the control design, thus increasing the system performance, the iterative learning control scheme is extended by parameter estimation of mathematical model through application of one form of sequential experimental design. The related sensor location problem corresponds to situation where from among all potential sites where the sensors can be placed a subset must be selected that provide the most informative measurements to update the system parameter estimates. The new results are lustrated by an example from the area of smart materials.