基于Ac-准则的最优输入设计方法

丁娣; 崔瀚; 汪清; 何开锋

doi:10.7638/kqdlxxb-2024.0138

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基于Ac-准则的最优输入设计方法

研究论文 | 更新时间：2026-05-28

- 基于Ac-准则的最优输入设计方法
- An Ac-criteria-based optimal input design method
- 空气动力学学报 2026年44卷第4期页码：77-87
- 作者机构：
  
  1. 中国空气动力研究与发展中心，计算空气动力研究所,绵阳,621000
  2. 中国空气动力研究与发展中心，空天技术研究所,绵阳,621000
- 作者简介：
  
  丁娣*（1981—），女，副研究员，研究方向：参数辨识. E-mail：dingdi2017@qq.com
- 基金信息：
- DOI：10.7638/kqdlxxb-2024.0138
  中图分类号： V212
- 纸质出版：2026
- 稿件说明：
移动端阅览
丁娣, 崔瀚, 汪清, 等. 基于Ac-准则的最优输入设计方法[J]. 空气动力学学报, 2026, 44(4): 77−87.

Ding Di, Cui Han, Wang Qing, et al. An Ac-criteria-based optimal input design method[J]. 2026, 44(4): 77-87.
丁娣, 崔瀚, 汪清, 等. 基于Ac-准则的最优输入设计方法[J]. 空气动力学学报, 2026, 44(4): 77−87. DOI： 10.7638/kqdlxxb-2024.0138

Ding Di, Cui Han, Wang Qing, et al. An Ac-criteria-based optimal input design method[J]. 2026, 44(4): 77-87. DOI： 10.7638/kqdlxxb-2024.0138.

摘要

针对飞机闭环回路激励信号输入设计需求，本文基于标模飞机AVM（aerodynamic validation model）平飞算例开展了典型最优准则函数与参数辨识精准度的关联性分析，设计了Ac-优化准则，结合粒子群算法形成了最优输入设计方法，并将该方法应用于“3211”、偶极方波和多正弦3种不同输入信号的参数优化设计。最后利用AVM飞机闭环仿真算例，对比了基于Ac-准则的最优设计方法和频带输入设计方法在3种输入激励下的飞机纵向气动导数辨识结果均方根误差，发现对于5个纵向气动导数的总体辨识精准度，基于Ac-准则设计的3种输入信号相对频带设计信号分别有27.6%、91.54%和64.4%的提升，且优化设计的输入激励信号有效改善了升力操纵导数的辨识精准度，说明对于闭环系统最优输入激励信号优化问题，该方法相对频带输入设计更为有效。

Abstract

Optimal input design enhances aerodynamic parameter identification accuracy for aircraft closed-loop systems. This paper adressed the optimal criterion-based input design problem for standard aircraft aerodynamic parameter identification. The design of global optimization of Fisher information inverse matrix's metric can help obtain the optimal input signal. Simple analysis or experience in many research works choose the matrix's metric. We used an aerodynamic validation standard model aircraft (AVM) cruise flight scenario to analyze the correlation between typical optimal criteria and parameter identification accuracy. The results show that the inverse matrix's trace and condition number correlate relatively more strongly with identification accuracy than other metrics. Thus

we proposed an Ac-criterion and use the particle swarm global algorithm to find the optimal parameter of an input signal to minimize the Ac-criterion in a given parameter range. Three excitation signals

namely

"3211"

dipole square wave

and multi-sine quadrature signal

were designed by the Ac-optimal method and the traditional frequency band analysis. The frequency band method uses the natural frequency of the aircraft directly to design the input signal's period and the empirical amplitude. Then

we utilized the designed signals to excite the AVM closed-loop's dynamic characteristics and generated flight simulation data to identify the aircraft's longitudinal aerodynamic stability and control derivatives. The Ac-optimal design method's effectiveness was evaluated by the estimated results' root-mean-square errors relative to the reference derivatives. The results show that the Ac-criterion-based optimization method is more efficient than the frequency band design method in finding the optimal signal to excite the closed-loop's dynamic characteristics. Compared with the band-designed results

the Ac-optimal-designed signals improve the total accuracy of the five longitudinal control derivatives by 27.6%

91.54%

and 64.4%

respectively. This enhancement is mainly due to the Ac-optimal-designed signals significantly improves the identification accuracy of the lift control derivative. This paper verified that criterion correlation analysis can help improve the input signal's excitation effect on the dynamic systems

and the proposed Ac-criterion-based design method is effective in obtaining the optimal input excitation signals. This method has some application values for identifying aerodynamic parameters for aircraft closed-loop systems.

关键词

Keywords

references

Levin M. Optimum estimation of impulse response in the presence of noise[J]. IRE Transactions on Circuit Theory, 1960, 7(1): 50−56. doi: 10.1109/TCT.1960.1086622

Ng T S, Qureshi Z H. Optimal input design for parameter estimation for an autoregressive model with output power constraints[J]. IFAC Proceedings Volumes, 1981, 14(2): 643−648. doi: 10.1016/S1474-6670(17)63557-2

Keesman K J. Optimal input design for parameter estimation in nonlinear state-space models using Pontryagin's minimum principle[J]. IFAC-PapersOnLine, 2015, 48(28): 1319−1324. doi: 10.1016/j.ifacol.2015.12.314

Keesman K J. Optimal input design for minimum-variance estimation of parameters in nonlinear state-space models[J]. IFAC-PapersOnLine, 2018, 51(15): 365−370. doi: 10.1016/j.ifacol.2018.09.172

Zhang Yunfeng, Wang Jianhong. Optimal input signal design for errors-in-variables closed-loop system parameter identification[J]. Journal of Nanchang University (Engineering Technology), 2022, 44(3): 297−302(in Chinese). doi: 10.13764/j.cnki.ncdg.2022.03.006

Klein V. Optimal input design for aircraft parameter estimation using dynamicprogramming principles[C]//Proc of the 17th Atmospheric Flight Mechanics Conference, Portland, OR, USA. AIAA, 1990: AIAA1990-2801. doi: 10.2514/6.1990-2801

Morelli E A. Flight test of optimal inputs and comparison with conventional inputs[J]. Journal of Aircraft, 1999, 36(2): 389−397. doi: 10.2514/2.2469

Jauberthie C, Bournonville F, Coton P, et al. Optimal input design for aircraft parameter estimation[J]. Aerospace Science and Technology, 2006, 10(4): 331−337. doi: 10.1016/j.ast.2005.08.002

Hosseini S, Göttlicher C, Holzapfel F. Optimal input design for flight vehicle system identification via dynamic programming and the direct method for optimal control[C ] //Proc of the AIAA Scitech 2019 Forum, San Diego, California. AIAA, 2019: AIAA 2019-1319. doi: 10.2514/6.2019-1319

Hosseini B, Steinert A, Hofmann R, et al. Advancements in the theory and practice of flight vehicle system identification[J]. Journal of Aircraft, 2023, 60(5): 1419−1436. doi: 10.2514/1.c037269

Hosseini S, Holzapfel F. Optimal input design for flight vehicle system identification in frequency domain[C]//Proc of the AIAA SCITECH 2022 Forum, San Diego, CA Virtual. AIAA, 2022: AIAA 2022-2297. doi: 10.2514/6.2022-2297

Hosseini S, Botkin N, Diepolder J, et al. Robust optimal input design for flight vehicle system identification[C]//Proc of the AIAA Scitech 2020 Forum, Orlando, FL. AIAA, 2020: AIAA 2020-0290. doi: 10.2514/6.2020-0290

Zhu Xiangdong, Cui Pingyuan, Wu Yaohua. Muti-step signal design for aircraft parameter identification[J]. Flight Dynamics, 1993, 11(2): 57−63(in Chinese). doi: 10.13645/j.cnki.f.d.1993.02.008?

Wang Qing, Qian Weiqi, He Kaifeng. Aerodynamic parameter identification and optimal input design for missile[J]. Journal of Astronautics, 2008, 29(3): 789−793(in Chinese). doi: 10.3873/j.issn.1000-1328.2008.03.010

Morelli E A. Flight-test experiment design for characterizing stability and control of hypersonic vehicles[J]. Journal of Guidance, Control, and Dynamics, 2009, 32(3): 949−959. doi: 10.2514/1.37092

Morelli E A, Smith M S. Real-time dynamic modeling: data information requirements and flight-test results[J]. Journal of Aircraft, 2009, 46(6): 1894−1905. doi: 10.2514/1.40764

MORELLI E A. Multiple input design for real-time parameter estimation in the frequency domain[C]. Paper REG-360, 13th IFAC Symposium on System Identification, Rotterdam, The Netherlands, August 2003.

Morelli E A. Practical aspects of real-time modeling for the learn-to-fly concept[C]//Proc of the 2018 Atmospheric Flight Mechanics Conference, Atlanta, Georgia. AIAA, 2018: AIAA 2018-3309. doi: 10.2514/6.2018-3309

Morelli E A. Autonomous real-time global aerodynamic modeling in the frequency domain[C]//Proc of the AIAA Scitech 2020 Forum, Orlando, FL. AIAA, 2020: AIAA 2020-0761. doi: 10.2514/6.2020-0761

Grauer J, Morelli E. Method for real-time frequency response and uncertainty estimation[J]. Journal of Guidance, Control, and Dynamics, 2014, 37(1): 336−344. doi: 10.2514/1.60795

Brandon J, Derry S, Heim G, et al. Ares-I-X stability and control flight test: analysis and plans[C]//Proc of the AIAA SPACE 2008 Conference Exposition, San Diego, California. AIAA, 2008: AIAA 2008-7807. doi: 10.2514/6.2008-7807

Morelli E A. Practical aspects of multiple-input design for aircraft system identification flight tests[C]//Proc of the AIAA AVIATION 2021 FORUM, VIRTUAL EVENT. AIAA, 2021: AIAA 2021-2795. doi: 10.2514/6.2021-2795

Lichota P, Sibilski K, Ohme P. D-optimal simultaneous multistep excitations for aircraft parameter estimation[J]. Journal of Aircraft, 2017, 54(2): 747−758. doi: 10.2514/1.c033794

Yang Yajun, Zheng Yuxin, Liao Ying. Optimal inputs design for a class of parametric linearizable system identification[J]. Control Theory Applications, 2016, 33(7): 889−896(in Chinese). doi: 10.7641/CTA.2016.50506

Zheng Yuxin. Optimal input design method and aircraft parameter identification[D]. Changsha: National University of Defense Technology, 2018(in Chinese).

Liu Y, Karimi H R, Yu Z W. D-optimal design for parameter estimation in discrete-time nonlinear dynamic systems[J]. Mathematical Problems in Engineering, 2012, 2012: 296701. doi: 10.1155/2012/296701

Dette H, Melas V B, Shpilev P. T-optimal discriminating designs for Fourier regression models[J]. Computational Statistics Data Analysis, 2017, 113: 196−206. doi: 10.1016/j.csda.2016.06.010

Jauberthie C, Denis-Vidal L, Li Q C, et al. Optimal input design for parameter estimation in a bounded-error context for nonlinear dynamical systems[J]. Automatica, 2018, 92: 86−91. doi: 10.1016/j.automatica.2018.03.003

Kennedy J, Eberhart R. Particle swarm optimization[C]//Proc of the Proceedings of ICNN'95 - International Conference on Neural Networks, Perth, WA, Australia. IEEE, 2002: 1942-1948. doi: 10.1109/ICNN.1995.488968

Venter G, Sobieszczanski-Sobieski J. Particle swarm optimization[J]. AIAA Journal, 2003, 41(8): 1583−1589. doi: 10.2514/2.2111

Fu Yangguang. Research on modified particle swarm optimization and their application in route planning for UAV[D]. Wuhan: Huazhong University of Science and Technology, 2011(in Chinese).

Ozcan E, Mohan C. Analysis of a simple particle swarm optimization system[J]. Intelligent Engineering Systems Through Artificial Neural Networks, 1998, 8: 253−258.

Hua Jun, Zhong Min, Zheng Sui, et al. Design and database applications of CAE-AVM model cruise configuration[J]. Acta Aerodynamica Sinica, 2022, 40(4): 132−147(in Chinese). doi: 10.7638/kqdlxxb-2022.0012

Wang Hao, Hua Jun, Zhong Min. Wind-tunnel test of cruise configuration of CAE-AVM model[J]. Acta Aerodynamica Sinica, 2022, 40(4): 148−157(in Chinese). doi: 10.7638/kqdlxxb-2022.0015

Xu Xiufeng. Closed-loop identification of aerodynamic parameters for hypersonic vehicle[D]. Harbin: Harbin Institute of Technology, 2014(in Chinese).

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