Generalization of norm optimal ILC for nonlinear systems with constraints

Abstract

This paper discusses a generalization of norm optimal iterative learning control (ilc) for nonlinear systems with constraints. The conventional norm optimal ilc for linear time invariant systems formulates an update equation as a closed form solution of the minimization of a quadratic cost function. In this cost function the next trial's tracking error is approximated by implicitly adding a correction to the model. The proposed approach makes two adaptations to the conventional approach: the model correction is explicitly estimated, and the cost function is minimized using a direct optimal control approach resulting in nonlinear programming problems. An efficient solution strategy for such problems is developed, using a sparse implementation of an interior point method, such that long data records can be efficiently processed. The proposed approach is validated experimentally.