一种航空发动机导叶角度调节器控制方法研究

航空发动机静子导流叶片角度数字电子控制系统的性能和可靠性对发动机的正常工作十分重要；为获得发动机的最优性能,提高飞行可靠性,并保证压气机工作稳定性,文章提出了一种基于RBF (Radial Basis Function)神经网络的PID控制器,构建了3层神经网络数学模型；在AMESim软件平台上,建立了该航空发动机导叶控制系统的数学模型,在Matlab/Simulink中搭建了RBF神经网络控制器；仿真结果表明,在相同参数设置下,本文所设计的控制器与传统PID控制器相比能够实现导叶角度调节器作动筒位移的更加快速、精确控制,表明该控制器设计方法是可行、有效的.

One kind of Aero-Engine Guide Vane Angle Regulator Control Method

Aero-engine stator guide vanes angle regulator digital electronic control system performance and reliability of the engine’s normal work is very important.In this article the controller based on RBF(Radial Basis Function) neural network PID is proposed in order to obtain optimum engine performance,improve flight reliability,and to ensure the stability of the compressor work,the establishment of three-layer neural network mathematical model.The aero-engine guide vane control system mathematical model is created under AMESim software and the RBF neural network controller model is built in Matlab/Simulink.Simulation results show that the designed controller in this article can achieve the guide vane angle actuator displacement regulator of more rapid control and accurate track compared with the traditional PID controller in the same parameter settings,indicating that the controller design method is feasible and effective.