Skip to main content
SpringerLink
Log in

A Perspective Analysis on Effects of Varying Inputs on UAV Model Estimation

  • Regular paper
  • Published:
Journal of Intelligent & Robotic Systems Aims and scope Submit manuscript

Abstract

The aviation use of Unmanned Aerial Vehicles (UAVs) necessitates a strong control design. The key to designing a durable control system is a well-developed flight dynamics model. System-based model identification techniques provide a useful means for estimating UAV modal parameters thereby saving time and money. Although considerable research is performed on UAV flight dynamics analysis utilizing system identification techniques, limited work exists that compares various techniques of system identification for UAVs in an exhaustive manner. Moreover, the research contributions toward performance evaluation and comparison of system identification methods outputs are even more scarce, especially under varying environmental conditions. In this study, a comprehensive framework utilizing various linear and nonlinear estimation techniques estimates unknown UAV model dynamics. To analyze the effects of varying flight conditions, a detailed analysis is performed which includes a parametric sweep of environmental elements as input arguments to the estimation process. The comparison involves the estimation of key performance parameters such as residual analysis, final prediction error, and fit percentages. Through rigorous analysis, it is demonstrated that the proposed framework predicts system parameters under a variety of conditions, thereby confirming its validity. It has also been demonstrated that a parametric sweep of environmental conditions, can be utilized to improve the authenticity of models’ data learning ranges, and their response to the prediction parameters. This paper, to the best of our knowledge, provides an elaborate platform for researchers to carry out comprehensive model prediction under a wide range of environmental conditions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Data Availability

The authors of this paper, for the best interest of the community, are willing to upload complete data-set in the repository, in case the research article is accepted for publication in the Journal. Moreover, the data-sets generated during the current study are also available from the corresponding author on reasonable request.

Abbreviations

\(\bar{c}\) :

Mean aerodynamic chord (m)

\(C_D\) :

Drag Coefficient

\(C_Y\) :

Side force Coefficient

C \(C_L\) :

Lift Coefficient

\(C_l\) :

Roll moment coefficient

\(C_m\) :

Pitch moment coefficient

\(C_n\) :

Yaw moment coefficient

D:

Drag (N)

g:

Gravitational acceleration (\(ms^{-2}\))

GCM:

Guidance and Control Module

h:

Altitude (m)

UAV:

Unmanned Aerial Vehicle

\(J_x,J_y,J_z\) :

Inertia matrix components in body frame

\(V_t\) :

Free-stream velocity (m/s)

L:

Lift (N)

m:

Vehicle mass (kg)

n,m,l:

Aerodynamic moment components for yaw, pitch and roll moments respectively, defined in body frame (Nm)

\(P_e,P_n\) :

Position coordinates along the inertial east and north directions (m)

p:

Roll rate in body frame (deg/sec)

q:

Pitch rate in body frame (deg/sec)

r:

Yaw rate in body frame (deg/sec)

\(q_{\infty }\) :

Free stream dynamic pressure \((N/m^2)\)

\(S_{Ref}\) :

Reference area \((m^2)\)

\(X_A,Y_A,Z_A\) :

Aerodynamic force components (axial, side and tangential force respectively) in the body frame (N)

T:

Engine thrust (N)

U:

Linear velocity along body x-axis (m/s)

V:

Linear velocity along body y-axis (m/s)

W:

Linear velocity along body z-axis (m/s)

W:

Weight (N)

ARX:

Automatic Regression eXogenous

ARMAX:

Automatic Regression Moving Average eXogenous

BJ:

Box Jenkin’s

OE:

Output Error

SS:

State Space

TP:

Tree Partition

WL:

WaveLet Network

NN:

Neural Network

FPE:

Final Prediction Error

MSE:

Mean Squared Error

n:

Model order for state space model

\(N_a\) :

Order of Polynomial A(q)

\(N_b\) :

Order of Polynomial B(q)

\(N_c\) :

Order of Polynomial C(q)

\(N_d\) :

Order of Polynomial D(q)

\(N_f\) :

Order of Polynomial F(q)

\(\rho \) :

Air density \((kg/m^3)\)

\(\beta \) :

Side slip angle (deg)

\(\alpha \) :

Aerodynamic angle of attack (deg)

\(\phi , \theta , \psi \) :

Roll, pitch and azimuth angles describing body frame w.r.t inertial frame (deg)

\(\gamma \) :

Flight path angle (deg)

\(\delta _a\) :

Aileron control

\(\delta _e\) :

Elevator control

\(\delta _f\) :

Flap control

References

  1. Chen, Y., Rosolia, U., Ames, A.D.: Decentralized task and path planning for multi-robot systems. IEEE Robotics and Automation Letters 6(3), 4337–4344 (2021)

    Article  Google Scholar 

  2. Gul, F., Mir, I., Gul, U., Forestiero, A.: A review of space exploration and trajectory optimization techniques for autonomous systems: Comprehensive analysis and future directions. In: Pervasive Knowledge and Collective Intelligence on Web and Social Media: First EAI International Conference, PerSOM 2022, Messina, Italy, November 17-18, 2022, Proceedings, pp. 125–138. Springer (2023)

  3. Paucar, C., Morales, L., Pinto, K., Sánchez, M., Rodríguez, R., Gutierrez, M., Palacios, L.: Use of drones for surveillance and reconnaissance of military areas. In: International Conference of Research Applied to Defense and Security, pp. 119–132. Springer (2018)

  4. van Lieshout, M., Friedewald, M.: Drones–dull, dirty or dangerous? the social construction of privacy and security technologies. In: Socially Responsible Innovation in Security, pp. 37–55. Routledge (2018)

  5. Din, A.F.U., Mir, I., Gul, F., Nasar, A., Rustom, M., Abualigah, L.: Reinforced learning-based robust control design for unmanned aerial vehicle. Arab. J. Sci. Eng. 1–16 (2022)

  6. Gul, F., Mir, S., Mir, I.: Coordinated multi-robot exploration: Hybrid stochastic optimization approach. In: AIAA SCITECH 2022 Forum, p. 1414 (2022)

  7. Gul, F., Mir, S., Mir, I.: Multi robot space exploration: A modified frequency whale optimization approach. In: AIAA SCITECH 2022 Forum, p. 1416 (2022)

  8. Wadood, A., Anavatti, S., Hassanein, O.: Robust controller design for an autonomous underwater vehicle. In: 2017 Ninth International Conference on Advanced Computational Intelligence (ICACI), pp. 237–244. IEEE (2017)

  9. Gul, F., Rahiman, W., Alhady, S.N., Ali, A., Mir, I., Jalil, A.: Meta-heuristic approach for solving multi-objective path planning for autonomous guided robot using pso–gwo optimization algorithm with evolutionary programming. J. Ambient. Intell. Humaniz. Comput. 1–18 (2020)

  10. Gul, F., Alhady, S.S.N., Rahiman, W.: A review of controller approach for autonomous guided vehicle system. Indonesian Journal of Electrical Engineering and Computer Science 20(1), 552–562 (2020)

    Article  Google Scholar 

  11. Mir, I., Eisa, S.A., Taha, H., Maqsood, A., Akhtar, S., Islam, T.U.: A stability perspective of bioinspired unmanned aerial vehicles performing optimal dynamic soaring. Bioinspiration & Biomimetics 16(6), 066010 (2021)

    Article  Google Scholar 

  12. Lai, Y.C., Chan, K.C., Liu, Y.C., Hsiao, F.B.: Development of an automatic landing system based on adaptive fuzzy logic control for fixed-wing unmanned aerial vehicles. Journal of Aeronautics, Astronautics and Aviation 48(3), 183–194 (2016)

    Google Scholar 

  13. Prach, A., Gürsoy, G., Yavrucuk, L.: Nonlinear controller for a fixed-wing aircraft landing. In: 2019 American Control Conference (ACC), pp. 2897–2902. IEEE (2019)

  14. Hussain, A., Hussain, I., Mir, I., Afzal, W., Anjum, U., Channa, B.A.: Target parameter estimation in reduced dimension stap for airborne phased array radar. In: 2020 IEEE 23rd International Multitopic Conference (INMIC), pp. 1–6. IEEE (2020)

  15. Hussain, A., Anjum, U., Channa, B.A., Afzal, W., Hussain, I., Mir, I.: Displaced phase center antenna processing for airborne phased array radar. In: 2021 International Bhurban Conference on Applied Sciences and Technologies (IBCAST), pp. 988–992. IEEE (2021)

  16. ud Din, A.F., Mir, I., Gul, F., Mir, S., Saeed, N., Althobaiti, T., Abbas, S.M., Abualigah, L.: Deep reinforcement learning for integrated non-linear control of autonomous uavs. Processes 10(7), 1307 (2022)

  17. Fatima, S.K., Abbas, M., Mir, I., Gul, F., Mir, S., Saeed, N., Alotaibi, A.A., Althobaiti, T., Abualigah, L.: Data driven model estimation for aerial vehicles: A perspective analysis. Processes 10(7), 1236 (2022)

    Article  Google Scholar 

  18. Gul, F., Mir, I., Mir, S.: Efficient environment exploration for multi agents: A novel framework. In: AIAA SCITECH 2023 Forum, p. 1088 (2023)

  19. Gul, F., Mir, I., Mir, S.: Aquila optimizer with parallel computing strategy for efficient environment exploration. J. Ambient. Intell. Humaniz. Comput. 1–16 (2023)

  20. Gul, F., Mir, I., Mir, S.: Reinforced whale optimizer for ground robotics: A hybrid framework. In: AIAA SCITECH 2023 Forum, p. 1601 (2023)

  21. Gul, F., Mir, I., Abualigah, L., Mir, S., Altalhi, M.: Cooperative multi-function approach: A new strategy for autonomous ground robotics. Futur. Gener. Comput. Syst. 134, 361–373 (2022)

    Article  Google Scholar 

  22. Gul, F., Mir, S., Mir, I.: Reinforced whale optimizer for multi-robot application. In: AIAA SCITECH 2022 Forum, p. 1416 (2022)

  23. Din, A.F., Mir, I., Gul, F., Akhtar, S., Mir, S.: Development of intelligent control strategy for an unconventional uav: A novel approach. In: AIAA SCITECH 2023 Forum, p. 1074 (2023)

  24. Din, A.F., Mir, I., Gul, F., Mir, S.: Non-linear intelligent control design for unconventional unmanned aerial vehicle. In: AIAA SCITECH 2023 Forum, p. 1071 (2023)

  25. Kunpal, S.K., Abbas, S.M., Mir, I., Gul, F., Mir, S.: A comprehensive flight data based model prediction: Perspective analysis and comparison. In: AIAA SCITECH 2023 Forum, p. 2237 (2023)

  26. Fatima, S.K., Abbas, S.M., Mir, I., Gul, F., Forestiero, A.: Flight dynamics modeling with multi-model estimation techniques: A consolidated framework. J. Electr. Eng. Technol. 1–11 (2023)

  27. Fatima, K., Abbas, S.M., Mir, I., Gul, F.: Data based dynamic modeling and model prediction of unmanned aerial vehicle: A parametric sweep of input conditions. In: AIAA SCITECH 2023 Forum, p. 1682 (2023)

  28. Belge, E., Hızır, K., Parlak, A., Altan, A., Hacioğlu, R.: Estimation of small unmanned aerial vehicle lateral dynamic model with system identification approaches. Balkan Journal of Electrical and Computer Engineering 8(2), 121–126 (2020)

    Article  Google Scholar 

  29. Altan, A., Aslan, Ö., Hacıoğlu, R.: Model predictive control of load transporting system on unmanned aerial vehicle (uav). In: Fifth international conference on advances in mechanical and robotics engineering, pp. 1–4. Institute of Research Engineers and Doctors Rome, Italy (2017)

  30. Dube, C., Pedro, J.O.: Modelling and closed-loop system identification of a quadrotor-based aerial manipulator. In: Journal of Physics: Conference Series, vol. 1016, p. 012007. IOP Publishing (2018)

  31. Nex, F., Remondino, F.: Uav for 3d mapping applications: a review. Applied geomatics 6, 1–15 (2014)

    Article  Google Scholar 

  32. Cai, G., Dias, J., Seneviratne, L.: A survey of small-scale unmanned aerial vehicles: Recent advances and future development trends. Unmanned Systems 2(02), 175–199 (2014)

    Article  Google Scholar 

  33. Menouar, H., Guvenc, I., Akkaya, K., Uluagac, A.S., Kadri, A., Tuncer, A.: Uav-enabled intelligent transportation systems for the smart city: Applications and challenges. IEEE Commun. Mag. 55(3), 22–28 (2017)

    Article  Google Scholar 

  34. Srivastava, S., Narayan, S., Mittal, S.: A survey of deep learning techniques for vehicle detection from uav images. J. Syst. Architect. 117, 102152 (2021)

    Article  Google Scholar 

  35. Trexler, T.: Supersonic target drones. Ordnance 45(246), 882–885 (1961)

    Google Scholar 

  36. Mir, I.: Dynamics, numeric optimization and control of dynamic soaring maneuvers for a morphing capable unmanned aerial vehicle. Ph.D. thesis

  37. Mir, I., Eisa, S., Taha, H.E., Gul, F.: On the stability of dynamic soaring: Floquet-based investigation. In: AIAA SCITECH 2022 Forum, p. 0882 (2022)

  38. Mir, I., Eisa, S., Maqsood, A., Gul, F.: Contraction analysis of dynamic soaring. In: AIAA SCITECH 2022 Forum, p. 0881 (2022)

  39. Mir, I., Maqsood, A., Akhtar, S.: Optimization of dynamic soaring maneuvers to enhance endurance of a versatile uav. In: IOP Conference Series: Materials Science and Engineering, vol. 211, p. 012010. IOP Publishing (2017)

  40. Mir, I., Eisa, S.A., Taha, H., Maqsood, A., Akhtar, S., Islam, T.U.: A stability perspective of bio-inspired uavs performing dynamic soaring optimally. Bioinspir, Biomim (2021)

    Google Scholar 

  41. Hopping, B.M., Garrett, T.M.: Low speed airfoil design for aerodynamic improved performance of uavs (2018). US Patent 9,868,525

  42. Hajiyev, C., Vural, S.Y.: Lqr controller with kalman estimator applied to uav longitudinal dynamics (2013)

  43. Carnes, T.: A low cost implementation of autonomous takeoff and landing for a fixed wing uav (2014)

  44. Borup, K.T., Stovner, B.N., Fossen, T.I., Johansen, T.A.: Kalman filters for air data system bias correction for a fixed-wing uav. IEEE Trans. Control Syst. Technol. 28(6), 2164–2176 (2019)

    Article  Google Scholar 

  45. Mir, I., Akhtar, S., Eisa, S., Maqsood, A.: Guidance and control of standoff air-to-surface carrier vehicle. The Aeronautical Journal 123(1261), 283–309 (2019)

    Article  Google Scholar 

  46. Ahsan, J., Ahsan, M., Jamil, A., Ali, A.: Grey box modeling of lateral-directional dynamics of a uav through system identification. In: 2016 International Conference on Frontiers of Information Technology (FIT), pp. 324–329. IEEE (2016)

  47. Rasheed, A.: Grey box identification approach for longitudinal and lateral dynamics of uav. In: 2017 International Conference on Open Source Systems & Technologies (ICOSST), pp. 10–14. IEEE (2017)

  48. Saengphet, W., Tantrairatn, S., Thumtae, C., Srisertpol, J.: Implementation of system identification and flight control system for uav. In: 2017 3rd International Conference on Control, Automation and Robotics (ICCAR), pp. 678–683. IEEE (2017)

  49. Bnhamdoon, O.A.A., Mohamad Hanif, N.H.H., Akmeliawati, R.: Identification of a quadcopter autopilot system via box–jenkins structure. Int. J. Dyn. Control. 8(3), 835–850 (2020)

  50. Ayyad, A., Chehadeh, M., Awad, M.I., Zweiri, Y.: Real-time system identification using deep learning for linear processes with application to unmanned aerial vehicles. IEEE Access 8, 122539–122553 (2020)

    Article  Google Scholar 

  51. Munguía, R., Urzua, S., Grau, A.: Ekf-based parameter identification of multi-rotor unmanned aerial vehiclesmodels. Sensors 19(19), 4174 (2019)

    Article  Google Scholar 

  52. Puttige, V.R., Anavatti, S.G.: Real-time system identification of unmanned aerial vehicles: a multi-network approach. J. Comput. 3(7), 31–38 (2008)

  53. Gul, F., Rahiman, W.: An integrated approach for path planning for mobile robot using bi-rrt. In: IOP Conference Series: Materials Science and Engineering, vol. 697, p. 012022. IOP Publishing (2019)

  54. Carnes, T.W., Bakker, T.M., Klenke, R.H.: A fully parameterizable implementation of autonomous take-off and landing for a fixed wing uav. In: AIAA Guidance, Navigation, and Control Conference, p. 0603 (2015)

  55. You, D.I., Jung, Y.D., Cho, S.W., Shin, H.M., Lee, S.H., Shim, D.H.: A guidance and control law design for precision automatic take-off and landing of fixed-wing uavs. In: AIAA guidance, navigation, and control conference, p. 4674 (2012)

  56. Gul, F., Rahiman, W., Nazli Alhady, S.S.: A comprehensive study for robot navigation techniques. Cogent Engineering 6(1), 1632046 (2019)

    Article  Google Scholar 

  57. Szczepanski, R., Tarczewski, T.: Global path planning for mobile robot based on artificial bee colony and dijkstra’s algorithms. In: 2021 IEEE 19th International Power Electronics and Motion Control Conference (PEMC), pp. 724–730. IEEE (2021)

  58. Stevens, B.L., Lewis, F.L., Johnson, E.N.: Aircraft control and simulation: dynamics, controls design, and autonomous systems. John Wiley & Sons (2015)

  59. Stevens, B.L., Lewis, F.L., Johnson, E.N.: Aircraft dynamics and classical control design. Aircraft Control and Simulation: Dynamics, Controls Design, and Autonomous Systems, Third Edition 250–376 (2003)

  60. Din, A.F.U., Akhtar, S., Maqsood, A., Habib, M., Mir, I.: Modified model free dynamic programming: an augmented approach for unmanned aerial vehicle. Appl. Intell. 1–21 (2022)

Download references

Funding

This research was not funded by any institute.

Author information

Authors and Affiliations

  1. School of Avionics & Electrical Engineering, College of Aeronautical Engineering, National University of Sciences and Technology, Risalpur, Pakistan

    Syeda Kounpal Fatima & Imran Mir

  2. Department of Avionics Engineering, Air University, Aerospace and Aviation Campus Kamra, Attock City, Pakistan

    Manzar Abbas

  3. Department of Electrical Engineering, National University of Computer and Emerging Science, Peshawar Campus, Peshawar, Pakistan

    Suleman Mir

  4. Department of Electrical Engineering, Aerospace and Avionics Campus Kamra, Air University, Islamabad, Pakistan

    Faiza Gul

Authors
  1. Syeda Kounpal Fatima
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Manzar Abbas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Imran Mir
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Suleman Mir
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Faiza Gul
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The first author (S Kounpal Fatima) and second author (Syed Manzar Abbas) performed the research. The third, fourth, and fifth authors (Imran Mir, Suleman Mir, and Faiza Gul) directed and conducted the framework of the research.

Corresponding author

Correspondence to Imran Mir.

Ethics declarations

Ethics Approval

The authors of this paper endorse their approval in this regard.

Consent to Participate

Not Applicable.

Consent for Publication

The authors of this paper endorse their consent for publication.

Conflict of Interest

The authors of this paper declare none conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fatima, S.K., Abbas, M., Mir, I. et al. A Perspective Analysis on Effects of Varying Inputs on UAV Model Estimation. J Intell Robot Syst 108, 71 (2023). https://doi.org/10.1007/s10846-023-01889-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10846-023-01889-0

Keywords

Search

Navigation