基于QPSO-ELM的某型涡轴发动机起动过程模型辨识

doi:10.7527/S1000-6893.2018.22251

电子电气工程与控制

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基于QPSO-ELM的某型涡轴发动机起动过程模型辨识

伍恒, 李本威, 张赟, 杨欣毅

海军航空大学航空基础学院, 烟台 264001

收稿日期:2018-04-27 修回日期:2018-05-21 出版日期:2018-11-15 发布日期:2018-07-20
通讯作者: 李本威 E-mail:LBW103@sina.com
基金资助:
国家自然科学基金（51505492，61174031）；泰山学者建设工程专项经费

Dynamic model identification of starting process of a turbo-shaft engine based on QPSO-ELM

WU Heng, LI Benwei, ZHANG Yun, YANG Xinyi

Aviation Foundation College, Naval Aviation University, Yantai 264001, China

Received:2018-04-27 Revised:2018-05-21 Online:2018-11-15 Published:2018-07-20
Supported by:
National Natural Science Foundation of China (51505492, 61174031); Special Funds of Taishan Scholar Project

摘要/Abstract

摘要： 针对解析法建立某型涡轴发动机起动过程模型困难的问题，提出一种基于量子粒子群优化-极限学习机（QPSO-ELM）的某型涡轴发动机起动过程模型数据驱动辨识方法。首先构建基于状态空间法描述的某型涡轴发动机起动过程分段模型，然后结合发动机起动试验数据，采用QPSO-ELM算法对该起动模型进行辨识，试验结果表明：燃气发生器转子转速、发动机输出轴转速和燃气涡轮后温度的辨识结果都良好地逼近了实测数据，最大相对误差的均值分别为1.358%、1.628%和2.195%，满足实际应用的精度需求，并且QPSO-ELM的辨识精度优于极限学习机（ELM）、支持向量机（SVM）和反向传播（BP）神经网络。

关键词: QPSO, ELM, 涡轴发动机, 起动过程, 数据驱动, 模型辨识

Abstract: To solve the difficulty of establishing models for starting process of a certain type of turbo-shaft engine by analytical methods, a data-driven method for model identification of the starting process of a turbo-shaft engine based on the Extreme Learning Machine optimized by the Quantum-behaved Particle Swarm Optimization (QPSO-ELM) algorithm is proposed. Firstly, a subsection model for the starting process of the turbo-shaft engine is constructed in light of the description of the state space method. Then, the QPSO-ELM algorithm is adopted to identify the constructed model in combination with data of the engine starting test. The identification results of the speed of the gas generator rotor, the speed of the engine output shaft and the temperature of the gas turbine outlet are all close to measured data, the mean maximum relative errors are 1.358%, 1.628% and 2.195%, respectively, which can meet the precision requirement of practical application. In addition, the identification accuracy of the QPSO-ELM is better than the Extreme Learming Machine (ELM), the Support Vector Machine (SVM) and the Back Propagation (BP) neural network.

Key words: QPSO, ELM, turbo-shaft engine, starting process, data-driven, model identification

中图分类号:

V235.12

伍恒, 李本威, 张赟, 杨欣毅. 基于QPSO-ELM的某型涡轴发动机起动过程模型辨识[J]. 航空学报, 2018, 39(11): 322251-322261.

WU Heng, LI Benwei, ZHANG Yun, YANG Xinyi. Dynamic model identification of starting process of a turbo-shaft engine based on QPSO-ELM[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2018, 39(11): 322251-322261.

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E-mail：hkxb@buaa.edu.cn

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基于QPSO-ELM的某型涡轴发动机起动过程模型辨识

Dynamic model identification of starting process of a turbo-shaft engine based on QPSO-ELM

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