[1]安斯奇,刘晓锋,侯宽新,等. 轻微型无人机电动力系统动力学模型建立[J].哈尔滨理工大学学报,2020,25(03):33-39.[doi:1015938/jjhust202003006]
 AN Si qi,LIU Xiao feng,HOU Kuan xin,et al.Dynamic Modeling of Electric Power on Light UAV[J].哈尔滨理工大学学报,2020,25(03):33-39.[doi:1015938/jjhust202003006]
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 轻微型无人机电动力系统动力学模型建立()
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《哈尔滨理工大学学报》[ISSN:1007-2683/CN:23-1404/N]

卷:
25
期数:
2020年03期
页码:
33-39
栏目:
电气与电子工程
出版日期:
2020-06-25

文章信息/Info

Title:
Dynamic Modeling of Electric Power on Light UAV
文章编号:
1007-2683(2020)03-0033-07
作者:
 安斯奇1刘晓锋2侯宽新1徐星辰1
 1.中国民用航空飞行学院 航空工程学院,四川 广汉 618307; 
2.北京航空航天大学 交通科学与工程学院,北京 100191)
Author(s):
AN Siqi1LIU Xiaofeng2HOU Kuanxin1XU Xingchen1
 (1.Institute of Aeronautic Engineering, Civil Aviation Flight University of China, Guanghan 618307, China;
2.School of Transportation Science and Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China)
关键词:
 无人机电动力系统动力学建模参数辨识等惯量替代
Keywords:
 Keywords:unmanned aerial vehicle electric power plant dynamic modeling parameter identification equivalent replacement on inertial of moment
分类号:
TM331;V279
DOI:
1015938/jjhust202003006
文献标志码:
A
摘要:
 摘要:针对数字电子调速器、无刷直流电动机和定距螺旋桨组成的典型轻微型无人机的电动力系统,以推导和辨识的方法建立动力学模型。首先,结合组成部件的输入输出特性,考虑到轻微型无人机的低速工况,以部件级推导方法建立的电动力系统动力学模型由线性项和非线性项混合组成。其次,给出轻微型无人机电动力系统的动力学模型参数辨识方法。采用特制编码盘等惯量替代螺旋桨,使用动态响应结果辨识线性项参数,使用稳态关系辨识非线性项参数。最后,通过特制的地面测试台架,验证建模方法的正确性。
Abstract:
 Abstract:Focused on UAV(unmanned aerial vehicle) equipped electric power plant composed by Electric speed controllers, brushless DC motors and fixpitched propellers, a modeling method combined deduction with identification is studied Firstly, in consideration of typical flight modes, the dynamic model of electric power was deduced based on component characteristics, and the model shows a nonlinear differential formula with a linear term and a nonlinear term Secondly, an identification method is given To avoid the aerodynamical nonlinear term, the propeller was replaced by a specialized discshaped coder with equivalent inertial moment, and the linear parameters were identified directly through output response Afterwards, the nonlinear parameter was identified by static inand output signals Finally the method of dynamic modeling is proved to be effective on a ground test unit

参考文献/References:

[1]陶于金, 李沛峰. 无人机系统发展与关键技术综述[J].航空制造技术, 2014(20):34.
TAO Yujin, LI Peifeng. Development and Key Technology of UAV[J]. Aeronautical Manufacturing Technology, 2014(20): 34.
[2]REZENDE R N, BARROS E, PEREZ V. General Aviation 2025-a Study for Electric Propulsion[C]// 2018 Joint Propulsion Conference, Indianapolis, 2018:4900.
[3]JAHN R G. Physics of Electric Propulsion[M]. New York: Courier Corporation, 2006.
[4]KRO〖AKGˇ〗LU M T, NDER E. Experimental Modelling of Propulsion Transients of a Brushless DC Motor and Propeller Pair Under Limited Power Conditions: A Neural Network Based Approach[J]. IFAC Proceedings Volumes,2009,42(19):37.
[5]AHSUN U, BADAR T, TAHIR S, et al. RealTime Identification of PropellerEngine Parameters for Fixed Wing UAVS[J]. IFACPapers on Line, 2015, 48(28): 1082.
[6]BOUABDALLAH S, NOTH A, SIEGWART R. PID vs LQ Control Techniques Applied to an Indoor Micro Quadrotor[C]// Proc. of the IEEE International Conference on Intelligent Robots and Systems (IROS), Sendai,2004:2451.
[7]YANG S F, CHOU J H. A Mechatronic Positioning System Actuated Using a Micro DCMotorDriven PropellerThruster[J]. Mechatronics, 2009, 19(6): 912.
[8]SZAFRANSKI G, CZYBA R, BLACHUTA M. Modeling and Identification of Electric Propulsion System for Multirotor Unmanned Aerial Vehicle Design[C]// Unmanned Aircraft Systems (ICUAS), 2014 International Conference on IEEE, Orlando, 2014:470.
[9]PENG J, DUBAY R. Identification and Adaptive Neural Network Control of a DC Motor System with DeadZone Characteristics[J]. ISA Transactions, 2011, 50(4): 588.
[10]KRNETA R, ANTI〖XC6号大C.tif〗 S, STOJANOVI〖XC6号大C.tif〗 D. Recursive Least Squares Method in Parameters Identification of DC Motors Models[J]. FACTA UniversitatisSeries: Electronics and Energetics, 2005, 18(3): 467.
[11]魏彤,郭蕊.自适应卡尔曼滤波在无刷直流电动机系统辨识中的应用[J].光学精密工程, 2012,20(10):2308.
WEI Tong, GUO Rui. Application of Kalman Filtering to System Identification of Brushless DC Motor[J]. Optics and Precision Engineering, 2012, 20(10): 2308.
[12]石建飞,戈宝军,吕艳玲,等. 永磁同步电机在线参数辨识方法研究[J].电机与控制学报,2018,22(3):17. 
SHI Jianfei, GE Baojun, LV Yanling, et al. Research of Parameter Identification of Permanent Magnetsynchronous Motor on Line[J]. Electric Machines and Control, 2018, 22(3): 17.
[13]KRISHNAN R. Electric Motor Drives: Modeling, Analysis, and Control[M]. New Jersey: Prentice Hall, 2001.
[14]SAAB S S, KAEDBEY R A. Parameter Identification of a DC Motor: An Experimental Approach[C]// Electronics, Circuits and Systems, 2001.ICECS 2001, Malta, 2001(2):981.
[15]DERAFA L, MADANI T, BENALLEGUE A. Dynamic Modelling and Experimental Identification of Four Rotors Helicopter Parameters[C]// Industrial Technology, 2006. ICIT 2006. Bhubaneswar, 2006: 1834.
[16]BRISTEAU P J, MARTIN P, SALAN E, et al. The Role of Propeller Aerodynamics in the Model of a Quadrotor UAV[C]// Proceedings of the European Control Conference 2009, Budapest, Hungary, August 23-26, 2009:683.
[17]KUSHLEYEV A, MELLINGER D, KUMAR V. Towards a Swarm of Agile Micro Quadrotors[J]. Autonomous Robots, 2013, 35 (4): 287.
[18]RWIGEMA M K. Propeller Blade Element Momentum Theory with Vortex Wake Deflection[C]// Proceedings of the 27th Congress of the International Council of the Aeronautical Sciences, Nice, France, 2010: 19.
[19]CHEN H F. Pathwise Convergence of Recursive Identification Algorithms for Hammerstein Systems[J]. IEEE Transactions on Automatic Control, 2004, 49(10): 1641.
[20]范伟,林瑜阳,李钟慎.遗传算法优化的 BP 神经网络压电陶瓷蠕变预测[J].电机与控制学报,2018,22(7):91.
FAN Wei, LIN Yuyang, LI Zhongshen. Prediction Model of The Creep of Piezoceramic Based on BP Neural Network Optimized by Genetic Algorithm[J]. Electric Machines and Control, 2018, 22(7):91.

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备注/Memo

备注/Memo:
 
收稿日期: 2018-10-29
基金项目:
国家自然科学基金(61573035);中国民用航空飞行学院科研基金面上项目(J201849)
作者简介:
刘晓锋(1979—),男,博士,副教授;
侯宽新(1979—),男,硕士,副教授
通信作者:
安斯奇(1990—),男,硕士,讲师,Email:ansiqi@cafuceducn
更新日期/Last Update: 2020-10-13