Transformation of Neural State Space Models into LFT Models for Robust Control Design

This paper considers the extraction of linear state space models and uncertainty models from neural networks trained as state estimators with direct application to robust control. A new method for writing a neural state space model in a linear fractional transformation form in a non-conservative way is proposed, and it is demonstrated how a standard robust control law can be designed for a system described by means of a multi layer perceptron.