Design Methods of Robust Feedback Controllers by Training Neural Networks

Abstract

This paper proposes two efficient methods to design a robust feedback control system by use of neural networks. The first method is based on L₂ gain, and two different neural networks are used. The controller is trained to be robust as a result of competition between neural networks. The second method is based on MiniMax optimization, and is useful to treat parametric uncertainties. In both methods, robustness of the neural network can be quantified. It is very easy to combine proposed methods so that effective methods for various problems can be derived.