Neural network identifier with iterative learning for turbofan engine rotor speed control system

This paper proposed a new neural network algorithm with fuzzy iterative learning controller and applied to a certain turbofan engine rotor speed control system. A dynamic neural network was used to identify the plant on-line. The control signal was then calculated iteratively according to the responses of a reference model and the output of identified plant. A fuzzy logic block with four very simple rules was added to the loop to improve the overall loop properties. Experimental results demonstrate the proposed control strategy provides better disturbance rejection and transient properties than those achieved by conventional mechanical-hydraulic controller(MHC) and analogue engine electronic controller(AEEC). At the same time, it can improve transitional quality in control system, and meet the demands of high performance and high control accuracy in turbofan engine