本文關鍵詞： 航空發(fā)動機 動態(tài)模型 非線性系統(tǒng)辨識 NARX網(wǎng)絡　出處：《計算機工程與應用》2017年12期 　論文類型：期刊論文

【摘要】：針對航空發(fā)動機參數(shù)非線性動態(tài)特性,提出一種基于外部輸入非線性自回歸(NARX)神經(jīng)網(wǎng)絡的發(fā)動機參數(shù)動態(tài)辨識模型。主要思路是根據(jù)NARX網(wǎng)絡的非線性時序預測特性,結(jié)合發(fā)動機參數(shù)的穩(wěn)態(tài)和動態(tài)參數(shù),提出一種基于偏穩(wěn)態(tài)差值預測的NARX參數(shù)動態(tài)模型結(jié)構。設計了SP-P辨識結(jié)構,整定了模型內(nèi)部結(jié)構參數(shù)并建立N1(低壓轉(zhuǎn)子轉(zhuǎn)速)、N2(高壓轉(zhuǎn)子轉(zhuǎn)速)、EGT(渦輪后排氣溫度)參數(shù)非線性差分預測模型。最后依據(jù)某發(fā)動機試車樣本,對推桿加減速時N1、N2、EGT動態(tài)辨模型進行仿真。仿真結(jié)果表明,N2相對誤差小于0.2%,N1相對誤差小于0.3%,EGT相對誤差小于1℃,滿足發(fā)動機試車仿真需要。最后,將所建模型應用于某A320機務維修訓練器的發(fā)動機仿真系統(tǒng)。
[Abstract]:Aiming at the nonlinear dynamic characteristics of aero-engine parameters. A dynamic identification model of engine parameters based on external input nonlinear autoregressive neural network is proposed. The main idea is based on the nonlinear prediction characteristics of NARX neural network. Combining the steady and dynamic parameters of engine parameters, a dynamic model structure of NARX parameters based on partial steady-state difference prediction is proposed, and the SP-P identification structure is designed. The internal structural parameters of the model were set up and the N1 (low pressure rotor speed) was established. EGT (turbine exhaust temperature) parameter nonlinear differential prediction model. Finally, according to a test sample of an engine, the dynamic identification model of N _ (1) N _ (2) EGT during acceleration and deceleration of push rod is simulated. The simulation results show that. The relative error of N2 is less than 0.2 and N1 is less than 0.3 and EGT relative error is less than 1 鈩，

本文編號：1491061