Subspace Model Identification Method for Flight Dynamics of FixedWing Airplane

Abstract

Abstract: Abstract： Due to notable advantages of simplicity and high efficiency, the subspace model identification (SMI) method becomes an attractive modeling approach for flight dynamics of the airplane system. However, because of the nonlinearity in the airplane system, SMI based model identification approaches which are designed for linear systems always generate some modeling errors. To deal with this problem, a novel twostage system identification approach for flight dynamics of the fixedwing airplane system was proposed. An instrumental variable aided closedloop SMI was first adopted to obtain the approximate linear model of the airplane dynamics. Then, an extended state observer (ESO) able to estimate the nonlinear dynamics of the airplane system was constructed using the obtained linear model. Based on the estimates by the ESO, a group of neural networks were trained to identify the decentralized model of the system nonlinearity. Finally, the nonlinear, six degreeoffreedom (6DOF) model of a B747 airplane was applied to system identification test, and the results obtained verify the effectiveness of the proposed approach.

Key words: Key words： subspace model identification（SMI）, fixedwing airplane, instrumental variable, extended state observer（ESO）, neural network

CLC Number:

V 211
TP 13

QI Xiaohui1,WANG Jianchen2. Subspace Model Identification Method for Flight Dynamics of FixedWing Airplane[J]. Journal of Shanghai Jiaotong University, 2016, 50(04): 613-618.

References

［1］MCGRAIL A K. Onboard parameter identification for a small UAV［D］. West Virginia: West Virginia University, 2012. ［2］HATAMLEH K S. Development of UAV model parameters estimation methodology and a unique IMU［D］. New Mexico: New Mexico State University, 2010. ［3］DOROBANTU A, MURCH A, METTLER B, et al. System identification for small, lowcost, fixedwing unmanned aircraft［J］. Journal of Aircraft, 2013, 50(4): 1117. ［4］WANG Q, WU K, ZHANG T, et al. Aerodynamic modeling and parameter estimation from QAR data of an airplane approaching a highaltitude airport［J］. Chinese Journal of Aeronautics, 2012, 25(3): 361371. ［5］杨帆, 熊笑, 陈宗基, 等. 超小型直升机动力学模型的建模、辨识与验证［J］. 北京航空航天大学学报, 2010, 36(8): 913917. YANG Fan, XIONG Xiao, CHEN Zongji, et al. Modeling, system identification and validation of small rotorcraft based unmanned aerial vehicle［J］. Journal of Beijing University of Aeronautics and Astronautics, 2010, 36(8):913917. ［6］吴伟，陈仁良. 直升机纵、横向耦合动力学模型的参数辨识方法研究［J］. 振动工程学报, 2009, 22(3): 232236. WU Wei, CHEN Renliang. Identification method of longitudinal and lateral coupled dynamic model for helicopter［J］. Journal of Vibration Engineering, 2009, 22(3): 232236. ［7］宋彦国, 孙涛. 旋翼飞行器飞行动力学系统辨识建模算法［J］. 南京航空航天大学学报, 2011, 43(3): 387392. SONG Yanguo, SUN Tao. System identification modeling of rotorcraft flight mechanics［J］. Journal of Nanjing University of Aeronautics and Astronautics, 2011, 43(3): 387392. ［8］邵明强, 李广文, 徐恒. 基于子空间和PEM的无人直升机两阶段参数辨识［J］. 飞行力学, 2013, 31(4): 367371. SHAO Mingqiang，LI Guangwen，XU Heng. Twostage parameters identification method of unmanned helicopter based on subspace and PEM［J］. Flight Dynamics, 2013, 31(4): 367371. ［9］HUANG B, DING X S, QIN S J. Closedloop subspace identification: an orthogonal projection approach［J］. Journal of Process Control, 2005, 15(1): 5366. ［10］李幼凤. 闭环子空间辨识方法及其应用［D］. 杭州: 浙江大学信息科学与工程学院, 2010. ［11］FIKRET C, RAHMI A, CHINGIZ H. Aircraft icing detection, identification, and reconfigurable control based on Kalman filtering and neural networks［J］. ASCE Journal of Aerospace Engineering, 2008, 21(2): 5160. ［12］PARK W, LEE S M, LEE S H. Designing similarity measurement with distance measure and application on laterally directional mode flight test［J］. Journal of Central South University, 2012, 19(4): 10321039. ［13］韩京清. 一类不确定对象的扩张状态观测器［J］. 控制与决策, 1995, 10(1): 8588. HAN Jingqing. The “extended state observer” of a class of uncertain systems［J］. Control and Decision, 1995, 10(1): 8588.

[1]	ZENG Guozhi, WEI Ziqing, YUE Bao, DING Yunxiao, ZHENG Chunyuan, ZHAI Xiaoqiang. Energy Consumption Prediction of Office Buildings Based on CNN-RNN Combined Model [J]. Journal of Shanghai Jiao Tong University, 2022, 56(9): 1256-1261.
[2]	WU Shuchen, QI Zongfeng, LI Jianxun. Intelligent Global Sensitivity Analysis Based on Deep Learning [J]. Journal of Shanghai Jiao Tong University, 2022, 56(7): 840-849.
[3]	QUAN Daying, CHEN Yun, TANG Zeyu, LI Shitong, WANG Xiaofeng, JIN Xiaoping. Radar Signal Recognition Based on Dual Channel Convolutional Neural Network [J]. Journal of Shanghai Jiao Tong University, 2022, 56(7): 877-885.
[4]	QIN Pengfei (秦鹏飞), WANG Xiaoliang∗ (王晓亮). Construction on Aerodynamic Surrogate Model of Stratospheric Airship [J]. J Shanghai Jiaotong Univ Sci, 2022, 27(6): 768-779.
[5]	SU Hong1 (苏红), WU Bozhao2 (吴博钊), MAO Xuchu1∗ (茅旭初). Non-Line-of-Sight Multipath Detection Method for BDS/GPS Fusion System Based on Deep Learning [J]. J Shanghai Jiaotong Univ Sci, 2022, 27(6): 844-854.
[6]	LÜ Qibing (吕其兵), LIU Tianyuan (刘天元), ZHANG Rong (张荣), JIANG Yanan (江亚南), XIAO Lei (肖雷), BAO Jingsong∗ (鲍劲松). Generation Approach of Human-Robot Cooperative Assembly Strategy Based on Transfer Learning [J]. J Shanghai Jiaotong Univ Sci, 2022, 27(5): 602-613.
[7]	QIN Yichao, HUANG Limin, WANG Xiao, MA Xuewen, DUAN Wenyang, HAO Wei. Feasibility of Wave Measurement by Using a Sailing Buoy and the Artificial Neural Network Technique [J]. Journal of Shanghai Jiao Tong University, 2022, 56(4): 498-505.
[8]	ZHAO Yong, SU Dan. Rogue Wave Prediction Based on Four Combined Long Short-Term Memory Neural Network Models [J]. Journal of Shanghai Jiao Tong University, 2022, 56(4): 516-522.
[9]	LIU Min (刘敏), YI Ming (易鸣), WU Minghu∗ (武明虎), WANG Juan (王娟), HE Yu (何宇). Breast Pathological Image Classification Based on VGG16 Feature Concatenation [J]. J Shanghai Jiaotong Univ Sci, 2022, 27(4): 473-484.
[10]	JIANG Zhiguo (蒋志国), CHANG Qing∗ (常青). USSL Net: Focusing on Structural Similarity with Light U-Structure for Stroke Lesion Segmentation [J]. J Shanghai Jiaotong Univ Sci, 2022, 27(4): 485-497.
[11]	LU Guannan1 (卢冠男), WANG Mengling1∗ (王梦灵), FOX Tamara2, JIANG Peng3 (蒋鹏), JIANG Fusong3 (蒋伏松). Novel Indicators for Adverse Glycemic Events Detection Analysis Based on Continuous Glucose Monitoring Neural Network Predictive Models [J]. J Shanghai Jiaotong Univ Sci, 2022, 27(4): 498-504.
[12]	YUAN Xiaoqi (袁筱祺), ZHU Lelan (朱乐兰), XU Qiongfan(徐琼凡), GAO Wei (高玮). Risk Prediction Model of Gallbladder Disease in Shanghai Middle-Aged and Elderly People Based on Neural Networks [J]. J Shanghai Jiaotong Univ Sci, 2022, 27(2): 153-159.
[13]	NIE Rui, WANG Hongru. Fault Diagnosis of UAV Formation Actuator Based on Neural Network Observer [J]. Air & Space Defense, 2022, 5(2): 32-41.
[14]	DING Ming, MENG Shuai, WANG Shuheng, XIA Xi. Neural-Network-Based Adaptive Feedback Linearization Control for 6-DOF Wave Compensation Platform [J]. Journal of Shanghai Jiao Tong University, 2022, 56(2): 165-172.
[15]	LIU Xiuli, XU Xiaoli. A Fault Diagnosis Method Based on Feature Pyramid CRNN Network [J]. Journal of Shanghai Jiao Tong University, 2022, 56(2): 182-190.