An adaptive turbo-shaft engine modeling method based on PS and MRR-LSSVR algorithms  

作　　者：Wang Jiankang Zhang Haibo Yan Changkai Duan Shujing Huang Xianghua 

机构地区：[1]College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics [2]Aviation Motor Control System Institute, Aviation Industry Corporation of China

出　　处：《Chinese Journal of Aeronautics》2013年第1期94-103,共10页中国航空学报（英文版）

基　　金：co-supported by Aeronautical Science Foundation of China (No. 2010ZB52011);Funding of Jiangsu Innovation Program for Graduate Education (No.CXLX11_0213)

摘　　要：In order to establish an adaptive turbo-shaft engine model with high accuracy, a new modeling method based on parameter selection （PS） algorithm and multi-input multi-output recursive reduced least square support vector regression （MRR-LSSVR） machine is proposed. Firstly, the PS algorithm is designed to choose the most reasonable inputs of the adaptive module. During this process, a wrapper criterion based on least square support vector regression （LSSVR） machine is adopted, which can not only reduce computational complexity but also enhance generalization performance. Secondly, with the input variables determined by the PS algorithm, a mapping model of engine parameter estimation is trained off-line using MRR-LSSVR, which has a satisfying accuracy within 5＆. Finally, based on a numerical simulation platform of an integrated helicopter/ turbo-shaft engine system, an adaptive turbo-shaft engine model is developed and tested in a certain flight envelope. Under the condition of single or multiple engine components being degraded, many simulation experiments are carried out, and the simulation results show the effectiveness and validity of the proposed adaptive modeling method.In order to establish an adaptive turbo-shaft engine model with high accuracy, a new modeling method based on parameter selection （PS） algorithm and multi-input multi-output recursive reduced least square support vector regression （MRR-LSSVR） machine is proposed. Firstly, the PS algorithm is designed to choose the most reasonable inputs of the adaptive module. During this process, a wrapper criterion based on least square support vector regression （LSSVR） machine is adopted, which can not only reduce computational complexity but also enhance generalization performance. Secondly, with the input variables determined by the PS algorithm, a mapping model of engine parameter estimation is trained off-line using MRR-LSSVR, which has a satisfying accuracy within 5＆. Finally, based on a numerical simulation platform of an integrated helicopter/ turbo-shaft engine system, an adaptive turbo-shaft engine model is developed and tested in a certain flight envelope. Under the condition of single or multiple engine components being degraded, many simulation experiments are carried out, and the simulation results show the effectiveness and validity of the proposed adaptive modeling method.

关 键 词：Adaptive engine model Least square support vector regression machine Modeling method Parameter selection Turbo-shaft engine 

分 类 号：V235.1[航空宇航科学与技术—航空宇航推进理论与工程]