小型无人机气动参数辨识的新型HGAPSO算法
收稿日期: 2016-04-25
修回日期: 2016-06-15
网络出版日期: 2016-06-27
基金资助
国家“863”计划（2014AA2157）
HGAPSO: A new aerodynamic parameters identification algorithm for small unmanned aerial vehicles
Received date: 2016-04-25
Revised date: 2016-06-15
Online published: 2016-06-27
Supported by
National High-tech Research and Development Program of China (2014AA2157)
针对小型无人机（UAVs）研制中操稳特性和飞行控制律设计评估对气动参数辨识的需求，提出了一种混合遗传粒子群优化算法（HGAPSO）。该算法以粒子群优化算法（PSO）为主体，在粒子优化路径中，引入遗传算法（GA）的交叉变异操作，增强粒子群跳出局部最优的能力；同时采用Kent映射改进粒子种群的初始化，使初始种群在可行解空间内分布更加均匀，增强全局优化能力。基于仿真结果，依据辨识准度及辨识成功率，对比了HGAPSO、常规PSO和GA优化算法气动参数辨识的结果，然后用蒙特卡洛仿真测试随机观测噪声的影响，结果表明该算法兼备PSO算法高的搜索效率和GA算法的全局优化能力，对随机观测噪声不敏感。最后，通过设计小型UAV试飞示例进行综合应用评价，结果表明：HGAPSO算法基于真实试飞数据进行气动参数辨识取得了满意结果，具有良好的实用性。
邵干 , 张曙光 , 唐鹏 . 小型无人机气动参数辨识的新型HGAPSO算法[J]. 航空学报, 2017 , 38(4) : 120365 -120365 . DOI: 10.7527/S1000-6893.2016.0196
In the development of small unmanned aerial vehicles (UAVs), aerodynamic parameter identification is needed for stability and control analysis and flight control law assessment. An improved hybrid genetic and particle swarm optimization algorithm (HGAPSO) is proposed for aerodynamic parameter identification. In this algorithm, the particle swarm optimization algorithm (PSO) is used as the main body, and the cross-over and mutation operation of genetic algorithm (GA) is included into the optimization of particle path to enhance the ability to jump out of the local optimal path. Kent mapping is also used to improve the initial distribution of the particle population, and to make the distribution more uniform and then the optimization more global. Based on the simulation results, the HGAPSO, PSO and GA algorithms are compared in terms of accuracy of identified aerodynamic parameters and success rate of identification. Monte Carlo simulations are further conducted to evaluate the effect of random noises in the measured signals. The results show that HGAPSO can provide both high efficiency and globalization in optimization, and has good resistance against measured noises. Flight testing data acquired from a small UAV are used to comprehensively evaluate the HGAPSO algorithm,and the HGAPSO shows satisfactory ability to identify aerodynamic parameters based on the flight data.
[1] MELANSON M, CHANG M, BAKER W Ⅱ. Wind tunnel testing's future:A vision of the next generation of wind tunnel test requirements and facilities[C]//48th AIAA Aerospace Sciences Meeting. Reston:AIAA, 2010.
[2] SIMSEK O, TEKINALP O. System identification and handling quality analysis of a UAV from flight test data[C]//AIAA Atmospheric Flight Mechanics Conference. Reston:AIAA, 2015.
[3] 丁娣, 钱炜祺, 汪清. 飞机操稳特性大导数辨识及随机噪声影响分析[J]. 航空学报, 2015, 36(7):2177-2185. DING D, QIAN W Q, WANG Q. Identification of aircraft stability and control characteristics derivatives and analysis of random noises[J]. Acta Aeronautica et Astronautica Sinica, 2015, 36(7):2177-2185 (in Chinese).
[4] JIANG T Y, LI J, HUANG K. Longitudinal parameter identification of a small unmanned aerial vehicle based on modified particle swarm optimization[J]. Chinese Journal of Aeronautics, 2015, 28(3):865-873.
[5] CHASE A, ROBERT A. Flight testing small UAVs for aerodynamic parameter estimation[C]//AIAA Atmospheric Flight Mechanics Conference. Reston:AIAA, 2014.
[6] 吴伟, 陈仁良. 直升机悬停状态全耦合飞行动力学模型辨识方法[J]. 航空学报, 2011, 32(2):202-211. WU W, CHEN R L. Identification method for helicopter fully coupled flight dynamics model in hover condition[J]. Acta Aeronautica et Astronautica Sinica, 2011, 32(2):202-211 (in Chinese).
[7] 雷宇. 基于STA34的无人机模型辨识与自主飞行控制律设计[D]. 北京:北京航空航天大学, 2013:26-56. LEI Y. Model identification and autonomous flight control law design based on STA34 autopilot[D]. Beijing:Beihang University, 2013:26-56 (in Chinese).
[8] 蔡金狮, 汪清, 王文正. 飞行器系统辨识学[M]. 北京:国防工业出版社, 2003:125-216. CAI J S, WANG Q, WANG W Z. Flight vehicle system identification[M]. Beijing:National Defence Industry Press, 2003:125-216 (in Chinese).
[9] KLEIN V, EUGENE A. Aircraft system identification-theory and practice[M]. Reston:AIAA, 2006:27-221.
[10] RAVINDRA V. Flight vehicle system identification:A time domain methodology[M]. Reston:AIAA, 2006:59-261.
[11] BURCHETT A. Aerodynamic parameter identification for symmetric projectiles:Comparing gradient based and evolutionary algorithms[C]//AIAA Atmospheric Flight Mechanics Conference. Reston:AIAA, 2011.
[12] 钱炜祺, 汪清, 王文正. 遗传算法在气动力参数辨识中的应用[J]. 空气动力学报, 2003, 21(2):196-201. QIAN W Q, WANG Q, WANG W Z. Aerodynamic parameter identification using GA[J]. Journal of Aerodynamic, 2003, 21(2):196-201 (in Chinese).
[13] 张天姣, 汪清, 何开锋. 粒子群算法在气动力参数辨识中的应用[J]. 空气动力学报, 2010, 28(6):633-635. ZHANG T J, WANG Q, HE K F. Aerodynamic parameter identification using PSO[J]. Journal of Aerodynamic, 2010, 28(6):633-635 (in Chinese).
[14] 刘国平, 徐钦龙. 粒子群算法及其与遗传算法的比较[J]. 中南工业大学学报, 2003, 34(2):328-330. LIU G P, XU Q L. Comparing PSO and GA[J]. Journal of Central South University of Technology, 2003, 34(2):328-330 (in Chinese).
[15] IBRAHIM A S N, SELAMAT. A query optimization in relevance feedback using hybrid GA-PSO for effective web information retrieval[C]//2009 Third Asia International Conference on Modelling & Simulation. Piscataway, NJ:IEEE Press, 2009.
[16] HUANG H. Global path planning for autonomous robot navigation using hybrid metaheuristic GA-PSO algorithm[C]//2011 Proceedings of SICE Annual Conference (SICE). Piscataway, NJ:IEEE Press, 2011:1338-1343.
[17] SHAMA J, SINGHAL R S. Comparative research on genetic algorithm, particle swarm optimization and hybrid GA-PSO[C]//Computing for Sustainable Global Development. 2015:110-114.
[18] HOLLAND J H. Genetic algorithms[J]. Scientific American, 1992, 267(1):66-72.
[19] KENNEDY J, EBERHART R. Particle swarm optimization[C]//Proceedings of IEEE International Conference on Neural Networks. Piscataway, NJ:IEEE Press, 1995:1942-1948.
[20] 杨迪雄. 非线性函数的混沌优化方法比较研究[J]. 计算力学学报, 2004, 21(3):257-262. YANG D X. Comparative study on chaos optimization algorithm for nonlinear function[J]. Chinese Journal of Computational, 2004, 21(3):257-262 (in Chinese).
[21] PIXHAWK. Pixhawk autopilot introduction[EB/OL].[2016-04-15]. http://www.pixhawk.com/modules/pixhawk.
[22] GÖTTLICHER C. System identification of an unmanned aerial vehicle using maximum likelihood methods[D]. München:Technische Universität München, 2013:92-108.
/
| 〈 |
|
〉 |
CJA