Representing and Learning Unmodeled Dynamics with Neural Network Memories

A nonlinear model representation consisting of an interpolation of several local models, which are valid within certain operation regimes, is proposed. Using this representation, first principles models and black-box models like neural networks may be integrated. Only operation regimes of the plant not adequately modeled by first principles are being represented and learned by a neural network memory. The principle is illustrated by simulation examples.