航空发动机全包线最优PID控制器设计

Neutral Network Based Optimal PID Controller over Whole Envelope for an Aero-engine

研究了一种基于神经网络的航空发动机全包线PID控制器设计方法。首先在全包线内选定若干典型工况点,并通过遗传算法离线优化PID控制器参数。然后通过BP神经网络训练建立飞行高度和马赫数与PID参数的非线性映射关系,亦即建立起基于神经网络的航空发动机全包线最优PID控制器。最后,将该控制器应用于某型涡扇发动机稳态控制和飞行过程控制仿真,与原控制器比较,控制效果获得有效改善。

An aero-engine optimal PID control scheme was presented,which was suitable for the engine whole envelope with a neural network approximation.Firstly,the optimal PID controller parameters were designed by genetic algorithm（GA） for a certain turbofan engine simplified real-time simulation model around some design-points.Then according to these optimized PID controller parameters,a neural network approximation was trained to imitate the relation between the parameters（Kp,Ki,Kd） of local optimal controllers and the flight conditions（H,Ma）,so that a neutral network based optimal PID controller was obtained.Finally,this new controller was applied to a certain turbofan engine control system simulation for the steady control,acceleration control and whole flight envelope control.The simulation results show that the aero-engine control system has excellent characteristics in design points and whole flight envelope.In this approach,the controller has a simple structure,and is easy and practical for real-time realization.