ニューラルネットワークの学習によるロバストな制御系の構築法 Design Methods of Robust Feedback Controllers by Training Neural Networks
This paper proposes two efficient methods to design a robust feedback control system by use of neural networks. The first method is based on <I>L</I><SUB>2</SUB> 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.
システム制御情報学会論文誌 12(10), 625-632, 1999-10-15