Subspace Identification of Process Dynamics for Iterative Learning Control

A new approach to subspace identification of pro- cess dynamics for iterative learning control is developed. The unknown process state-space model and noise covariance matrices are determined based on merged data from all available trials. A gantry robot used for benchmarking of iterative learning control algorithms provides the data for this work.