【摘要】 针对航空发动机这样的多变量控制对象,要解决的突出问题是输入变量对输出变量的交叉影响,介绍了单神经元进行多变量系统解耦控制的基本方法,采用改进的Hebb学习算法以加速收敛。对某涡喷发动机的数学模型进行了双变量单神经元PID控制仿真研究,结果表明:采用此算法构成的神经网络PID控制对地面模型和高空模型都具有完全解耦、响应速度快、稳态误差小、算法简单的优点;用两个神经元作为双变量控制器,可以使整个飞行包线内的控制器数目明显减少。更多还原

【Abstract】 Aero-engine is a kind of multi-variable plant,which is difficult to control with the possible interaction between input variable and output variables.This paper introduces a multi-variable decoupling control with single neuron,adopting the modified Hebb learning algorithm to fast convergence.The turbojet engine is further simulated on-the-ground and in-the-air,which proves several advantages of this control system such as complete decoupling,fast response,small static error,and easy algorithm.And the author concludes that using two single neurons as Dual-Variable controller is effective to reduce the number of controller all over the flight envelope.更多还原