Aeroengine Adaptive Modeling And Neural Network Control

To improve the adaptive capability of aeroengine control system, the research on mathematic model and controller has been conducted in present thesis. The work includes two parts: research on adaptive modeling and research on neural network control. The main idea of setting up adaptive real-time model of aeroengine believes that outputs of aeroengine will bias their nominal values in any case of off-nominal work. These biases include the off-nominal information of engine. We can design Kalman filter module and neural network module to obtain these biases on-line and real-time. Then these biases can be used to modify the outputs of onboard component-level model which is set up with nominal characteristic. After modification, outputs of onboard model are the same as those of the real engine, and the real time onboard model has the ability of adaptation. In the second part of thesis, two neural network control schemes are discussed: the PID control based on BP neural network identification, and the parallel control based on BP neural network and PID. The first scheme uses the nonlinear mapping ability of neural network to realizes the best parameters combination of PID. The second scheme uses BP neural network as forward controller and PID as feedback controller to realize parallel control. Both schemes take full advantage of tuning neural network's weights on-line to enhance the adaptive capability of controller. The controller using CMAC neural network is also studied in present thesis. It's used to control linear models of certain type aeroengine. The simulation results of the control system using CMAC neural network are satisfying.