Neural network controller design with a performance evaluation level

성능평가 계층을 가지는 신경망제어기 설계

We propose a new control architecture which consists of a PI controller and a neural network(NN) controller connected together in parallel. This architecture is well adapted to a wide range of uncertainties and variations of systems. The NN controller is learned through weights of the emulator which identify the dynamic chracteristics of the systems. A performance evaluation level of two NN's decides automatically which controller of the two controllers will be used mainly. The PI controller operates mainly during learning phase of the NN controller whereas a good performance is obtained from the NN controller only, when the NN controller is learned sufficiently.