基于K-均值聚类与粒子群核极限学习机的推力估计器设计

推进技术 ›› 2019, Vol. 40 ›› Issue (2): 259-266.

基于K-均值聚类与粒子群核极限学习机的推力估计器设计

赵姝帆¹,李本威¹,宋汉强²,逄珊³,朱飞翔¹

海军航空大学航空基础学院，山东烟台 264001,海军航空大学航空基础学院，山东烟台 264001,海军装备研究院，上海 200436,鲁东大学信息与电气工程学院，山东烟台 264025,海军航空大学航空基础学院，山东烟台 264001

发布日期:2021-08-15
基金资助:
国家自然科学基金(51505492)；山东省自然科学基金(ZR2016FQ19)；泰山学者建设工程专项经费资助。

Thrust Estimator Design Based on K-Means Clustering and Particle Swarm Optimization Kernel Extreme Learning Machine

Aviation Foundation College，Naval Aviation University，Yantai 264001，China,Aviation Foundation College，Naval Aviation University，Yantai 264001，China,Naval Academy of Armament，Shanghai 200436，China,College of Information and Electrical Engineering，Ludong University，Yantai 264025，China and Aviation Foundation College，Naval Aviation University，Yantai 264001，China

Published:2021-08-15

摘要/Abstract

摘要： 鉴于航空发动机直接推力控制与健康管理需要高精度及高实时性的推力估计器，提出了一种基于K-均值聚类与粒子群优化的核极限学习机推力估计方法。采用K-均值聚类对全工况范围内的测量数据进行聚类，在每一个子类中，通过核极限学习机建立推力估计器，采用粒子群算法对核极限学习机的核参数和惩罚系数进行优化，利用了核极限学习机稳定性好、非线性拟合能力强的特点，实现了对发动机推力的估计。经涡扇发动机台架试车数据训练与测试表明，本推力估计方法平均预测时间为0.27ms，实时性满足机载在线状态评估和直接推力控制需求，且在估计精度上较现有方法存在一定优势。

关键词: 航空发动机；推力估计器； K-均值聚类；粒子群核极限学习机；直接推力控制

Abstract: In order to achieve direct thrust control and health management of aero-engines， the thrust estimator with high precision and high real-time is needed. A design method for thrust estimator was proposed， which was based on K-means clustering and particle swarm optimization (PSO) kernel extreme learning machine (KELM). Firstly， the K-means method was used to cluster the measured data in the whole behavior range. Then the thrust estimator model was established by KELM in each sub-class， and PSO was utilized to search the best kernel parameter and penalty coefficient. The stability and non-linear fitting ability of KELM were fully utilized to estimate engine thrust. Finally， the training and testing results of turbofan engine bench test data show that the average forecast time of thrust estimation method is 0.27ms， which meets the requirements of direct thrust control and airborne on-line state assessment， and it is more accurate than the existing methods.

Key words: Aero-engine；Thrust estimator；K-means clustering；Particle swarm optimization kernel extreme learning machine；Direct thrust control

赵姝帆,李本威,宋汉强,逄珊,朱飞翔. 基于K-均值聚类与粒子群核极限学习机的推力估计器设计[J]. 推进技术, 2019, 40(2): 259-266.

ZHAO Shu-fan¹,LI Ben-wei¹,SONG Han-qiang²,PANG Shan³,ZHU Fei-xiang¹. Thrust Estimator Design Based on K-Means Clustering and Particle Swarm Optimization Kernel Extreme Learning Machine[J]. Journal of Propulsion Technology, 2019, 40(2): 259-266.

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