International Journal of Engineering

International Journal of Engineering

Optimal Design of Excitation Inputs for Identifying the Dynamics of Fixed-wing Aircraft Considering Active Control in MIMO Systems

Document Type : Original Article

Authors
A. Mohseni 1
M. Mortazavi 2
N. Sayyaf 1
S. A. Askari 1
1 Department of Mechanical Engineering, University of Isfahan, Isfahan, Iran
2 Department of Electrical Engineering, University of Isfahan, Isfahan, Iran
10.5829/ije.2026.39.10a.20
Abstract
This study presents a systematic and optimal framework for designing excitation inputs for fixed-wing aircraft system identification, with a particular focus on multi-input multi-output (MIMO) systems under active flight control. Excitation signals such as pulse, doublet, and multi-step are commonly selected due to their simplicity and widespread use in practical applications. However, their selection is often empirical and may not provide optimal excitation for accurate and robust parameter estimation. The proposed methodology systematically determines the most effective input signals, designs their frequency content and amplitude characteristics, and evaluates their performance under both open- and closed-loop control conditions. This approach facilitates accurate parameter estimation across multiple identification techniques and is generalizable to other aircraft as well as potentially to other complex dynamic systems. The effectiveness of the proposed method is demonstrated through high-fidelity six-degree-of-freedom (6-DOF) simulations in MATLAB/Simulink, showing its capability to efficiently and accurately excite the relevant aircraft modes for precise model identification.

Graphical Abstract

Optimal Design of Excitation Inputs for Identifying the Dynamics of Fixed-wing Aircraft Considering Active Control in MIMO Systems
Keywords
Aircraft
System Identification
Excitation Input Design
Parameter Estimation
Multi-input Multi-output Systems
Closed Loop

Subjects

1.    Ayyad A, Chehadeh M, Awad MI, Zweiri Y. Real-time system identification using deep learning for linear processes with application to unmanned aerial vehicles. IEEE Access. 2020;8:122539-53. https://doi.org/10.1109/ACCESS.2020.3006277.   
2.    Belge E, Kaba H, Parlak A, Altan A, Hacıoğlu R. Estimation of small unmanned aerial vehicle lateral dynamic model with system identification approaches. Balkan Journal of Electrical and Computer Engineering. 2020;8(2):121-6. https://doi.org/10.17694/bajece.654499.  
3.    Hosseini B, Steinert A, Hofmann R, Fang X, Steffensen R, Holzapfel F, et al. Advancements in the theory and practice of flight vehicle system identification. Journal of Aircraft. 2023;60(5):1419-36. https://doi.org/10.2514/1.C037269.  
4.    Matt JJ, Chao H, Shawon MH, Svoboda BC, Hagerott SG. System Identification for Small Flying-Wing Unmanned Aircraft Using Open-Loop and Closed-Loop Flight Data. Journal of Aircraft. 2025:1-17. https://doi.org/10.2514/1.C038147. 
5.    Morelli EA, Klein V. Aircraft System Identification: Theory and Practice: Sunflyte Enterprises; 2016. https://doi.org/10.1017/S0001924000087194. 
6.    Tischler MB, Remple RK. Aircraft and rotorcraft system identification: American Institute of Aeronautics and Astronautics Reston; 2012. https://doi.org/10.2514/4.868207.
7.    Morelli EA. Practical aspects of multiple-input design for aircraft system identification flight tests. AIAA Aviation 2021 Forum. https://doi.org/10.2514/6.2021-2795. 
8.    Lichota P, Ohme P, Sibilski K. Simultaneous multi-step excitations for aircraft system identification. AIAA Atmospheric Flight Mechanics Conference; 2016. https://doi.org/10.2514/6.2016-3704. 
9.    Gim H, Lee B, Huh J, Kim S, Suk J. Longitudinal system identification of an avian-type UAV considering characteristics of actuator. International Journal of Aeronautical and Space Sciences. 2018;19:1017-26. https://doi.org/10.1007/s42405-018-0084-5.  
10.    Aslandoğan OH. Development of a Rotorcraft Time Domain System Identification Software: Middle East Technical University (Turkey); 2023. https://hdl.handle.net/11511/101957. 
11.    Han Y, Kim Y. Optimal input design for online parameter estimation for aircraft with multiple control surfaces. Engineering Optimization. 2011;43(5):559-80. https://doi.org/10.1080/0305215X.2010.502937.  
12.    Gresham JL, Simmons BM, Fahmi J-MW, Woolsey CA. Remote uncorrelated pilot inputs for nonlinear aerodynamic model identification from flight data. AIAA Aviation 2021 Forum. https://doi.org/10.2514/6.2021-2792.   
13.    Fu J, Huang J, Song L, Yang D. Experimental study of aircraft achieving dutch roll mode stability without weathercock stability. International Journal of Aerospace Engineering. 2020;2020(1):8971275. https://doi.org/10.1155/2020/8971275.   
14.    Gresham JL, Simmons BM, Fahmi J-MW, Hopwood JW, Woolsey CA. Remote Uncorrelated Pilot Input Excitation Assessment for Unmanned Aircraft Aerodynamic Modeling. Journal of Aircraft. 2023;60(3):955-67. https://doi.org/10.2514/1.C036942.    
15.    Roeser MS, Fezans N. Method for designing multi-input system identification signals using a compact time-frequency representation. CEAS Aeronautical Journal. 2021;12(2):291-306. https://doi.org/10.1007/s13272-021-00499-6.   
16.    Kapeel EH, Safwat E, Kamel AM, Khalil MK, Elhalwagy YZ, Hendy H. Physical modeling, simulation and validation of small fixed-wing UAV. Unmanned Systems. 2023;11(04):327-50. https://doi.org/10.1142/S2301385023500152.  
17.    Tischler MB. System identification methods for aircraft flight control development and validation. Advances in aircraft flight control.2018:35-69.  https://doi.org/10.21949/1403895.   
18.    Grauer JA, Boucher MJ. Aircraft system identification from multisine inputs and frequency responses. Journal of Guidance, Control, and Dynamics. 2020;43(12):2391-8. https://doi.org/10.2514/1.G005131.   
19.    Morelli EA. Optimal input design for aircraft stability and control flight testing. Journal of Optimization Theory and Applications. 2021;191(2):415-39. https://doi.org/10.1007/s10957-021-01912-0.   
20.    Hosseini S, Hofmann R, Holzapfel F, Rauleder J. Optimal Input Design for Aircraft Parameter Estimation-Flight Test Results. AIAA SCITECH 2025 Forum; 2025. https://doi.org/10.2514/6.2025-2173.  
21.    Lichota P. Multi-Axis Inputs for Identification of a Reconfigurable Fixed-Wing UAV. Aerospace. 2020;7(8):113. https://doi.org/10.3390/aerospace7080113.   
22.    Millidere M. Optimal input design and system identification for an agile aircraft: Middle East Technical University (Turkey); 2021. https://hdl.handle.net/11511/93115. 
23.    Mazhar MF, Wasim M, Abbas M, Riaz J, Swati RF. Aircraft system identification using multi-stage PRBS optimal inputs and maximum likelihood estimator. Aerospace. 2025;12(2):74. https://doi.org/10.3390/aerospace12020074.  
24.    Simsek O, Bayrak AI, Karatoprak S, Dogan A. Comparison of Open Loop and Closed Loop System Identification of a UAV. 2018 Atmospheric Flight Mechanics Conference; 2018. https://doi.org/10.2514/6.2018-3476.  
25.    Wang Z, Li A, Mi Y, Lu H, Wang C. Aircraft Closed-Loop Dynamic System Identification in the Entire Flight Envelope Range Based on Deep Learning. Journal of Aerospace Engineering. 2024;37(6):04024079. https://doi.org/10.1061/JAEEEZ.ASENG-5657. 

26.    Chu, T. S. C., Sorilla, J., & Chua, A. Y. UAV-Based Structural Health Monitoring Using a Two-Stage CNN Model with Lighthouse Localization in GNSS-Denied Environments. HighTech and Innovation Journal. 2025; 6(2), 398-410. https://doi.org/10.28991/HIJ-2025-06-02-03. 
27.    Jiang, Y., Pang, X., Zhang, Z., Jing, H., Wei, L., Su, J., & Zhang, P. Integrating Intelligent Sensors for Safe UAV Distribution: Design and Evaluation of Ranging System. HighTech and Innovation Journal. 2024; 5(3), 603-13. https://doi.org/10.28991/HIJ-2024-05-03-04.   
28.    Schmidt DK. Modern flight dynamics: McGraw-Hill New York; 2012.
29.    Stevens BL, Lewis FL, Johnson EN. Aircraft control and simulation: dynamics, controls design, and autonomous systems: John Wiley & Sons; 2015. https://doi.org/10.1002/9781119174882. 
30.    Roskam J. Airplane flight dynamics and automatic flight controls: DARcorporation; 1998.
31.    Bottigliero R, Rossano V, De Stefano G. Transonic Dynamic Stability Derivative Estimation Using Computational Fluid Dynamics: Insights from a Common Research Model. Aerospace. 2025;12(4):304. https://doi.org/10.3390/aerospace12040304.   
32.    Nguyen NT, Xiong J. CFD-Based Frequency Domain Method for Dynamic Stability Derivative Estimation with Application to Transonic Truss-Braced Wing. AIAA AVIATION 2022 Forum; 2022. https://doi.org/10.2514/6.2022-3596. 
33.    Shang T, Chen B, Yanling W, Ting Y, Hailiang L, Lixin W. Identification of aircraft longitudinal aerodynamic parameters using an online corrective test for wind tunnel virtual flight. Chinese Journal of Aeronautics. 2024;37(9):261-75. https://doi.org/10.1016/j.cja.2024.05.031.    
34.    Tai S, Wang L, Wang Y, Lu S, Bu C, Yue T. Identification of lateral-directional aerodynamic parameters for aircraft based on a wind tunnel virtual flight test. Aerospace. 2023;10(4):350. https://doi.org/10.3390/aerospace10040350.   
35.    Hassan AA-B. Calculating Aerodynamic & Stability Characteristics for Small fixed-wing UAVs based on Digital DATCOM. The International Undergraduate Research Conference; 2022: The Military Technical College. https://doi.org/10.21608/iugrc.2022.303029. 
36.    Ahmad M, Hussain ZL, Shah SIA, Shams TA. Estimation of stability parameters for wide body aircraft using computational techniques. Applied Sciences. 2021;11(5):2087. https://doi.org/10.3390/app11052087.    
37.    Granja DM. Hamiltonian Dynamics applied to aerobatic behavior of a high-performance airplane: Universidade do Porto (Portugal); 2024.
38.    Rezazadeh Movahhed S, Hamed MA. Aerodynamic coefficients computation for fixed wing aircrafts using datcom software with emphasize on rudderless flying-wing uavs. Journal of Aerospace Science and Technology. 2024;17(1):60-71. https://doi.org/10.22034/jast.2023.398894.1152.   
39.    Jategaonkar RV. Flight vehicle system identification: a time domain methodology: American Institute of Aeronautics and Astronautics; 2006. https://doi.org/10.1017/S0001924000011015.  
International Journal of Engineering
Volume 39, Issue 10
TRANSACTIONS A: Basics
October 2026
Pages 2602-2616