Flexible Linear-Fractional-Representation-Based Model Augmentation for Efficient Nonlinear System Identification

A flexible linear-fractional-representation (LFR) based model augmentation structure is proposed that can represent a wide range of existing model augmentation structures. An identification algorithm is developed to estimate ANN implementations of the proposed augmentation structure.