Skip to main content
SpringerLink
Log in

A Survey and Categorization of Small Low-Cost Unmanned Aerial Vehicle System Identification

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

Abstract

Remote sensing has traditionally be done with satellites and manned aircraft. While these methods can yield useful scientific data, satellites and manned aircraft have limitations in data frequency, process time, and real time re-tasking. Small low-cost unmanned aerial vehicles (UAVs) can bridge the gap for personal remote sensing for scientific data. Precision aerial imagery and sensor data requires an accurate dynamics model of the vehicle for controller development. One method of developing a dynamics model is system identification (system ID). The purpose of this paper is to provide a survey and categorization of current methods and applications of system ID for small low-cost UAVs. This paper also provides background information on the process of system ID with in-depth discussion on practical implementation for UAVs. This survey divides the summaries of system ID research into five UAV groups: helicopter, fixed-wing, multirotor, flapping-wing, and lighter-than-air. The research literature is tabulated into five corresponding UAV groups for further research.

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

References

  1. Abbeel, P., Ganapathi, V., Ng, A.: Learning vehicular dynamics, with application to modeling helicopters. Adv. Neural Inf. Process. Syst. 18, 1–8 (2006)

    Google Scholar 

  2. Abbeel, P., Coates, A., Quingley, M., Ng, A.Y.: An application of reinforcement learning to aerobatic helicopter flight. Adv. Neural Inf. Process. Syst. 19, 1–8 (2007)

    Google Scholar 

  3. Adiprawita, W., Ahmad, A., Sembiring, J.: Automated flight test and system identification for rotary wing small aerial platform using frequency responses analysis. J. Bionic Eng. 4(4), 7 (2007)

    Article  Google Scholar 

  4. Ahmad, S., Shaheed, M., Chipperfield, A., Tokhi, M.: Non-linear modelling of a one-degree-of-freedom twin-rotor multi-input multi-output system using radial basis function networks. Proc. Inst. Mech. Eng. 216(4), 197–208 (2002)

    Google Scholar 

  5. Atkins, E., Miller, R., Plet, T., Shaw, K., Ribbens, W., Washabugh, D.: Solus: an autonomous aircraft for flight control and trajectory planning research. In: Proceedings of the 1998 American Control Conference (1998)

  6. Bhandari, S., Colgren, R., Lederbogen, P., Kowalchuk, S.: Six-dof dynamic modeling and flight testing of a uav helicopter. In: Proceeding of the AIAA Modeling and Simulation Technologies Conference and Exhibit, pp. 992–1008 (2005)

  7. Bottasso, C., Leonello, D., Maffezzoli, A., Riccardi, F.: A procedure for the identification of the inertial properties of small-size uavs. In: XX AIDAA Congress (2009)

  8. Brunke, S., Campbell, M.: Estimation architecture for future autonomous vehicles. In: Proceedings of the 2002 American Control Conference, vol. 2, pp. 1108–1114 (2002)

  9. Budiyono, A., Yoon, K., Daniel, F.: Integrated identification modeling of rotorcraft-based unmanned aerial vehicle. In: Proceedings of the 17th Mediterranean Conference on Control and Automation, pp. 898–903 (2009)

  10. Cai, G., Chen, B., Peng, K.: Modeling and control of the yaw channel of a uav helicopter. IEEE Trans. Ind. Electron. 55(9), 3426–3434 (2008)

    Article  Google Scholar 

  11. Cai, G., Chen, B.M., Lee, T.H.: An overview on development of miniature unmanned rotorcraft systems. Front. Elect. Electron. Eng. 5(1), 1–14 (2009)

    Google Scholar 

  12. Carnduff, S.: System Identification of Unmanned Aerial Vehicles. Ph.D. thesis, Cranfield University (2008)

  13. Chao, H., Luo, Y., Di, L., Chen, Y.Q.: Roll-channel fractional order controller design for a small fixed-wing unmanned aerial vehicle. Control. Eng. Pract. 18(7), 761–772 (2010)

    Article  Google Scholar 

  14. Chen, Y., Wen, C., Dou, H., Sun, M.: Iterative learning identification of aerodynamic drag curve from tracking radar measurements. Control. Eng. Pract. 5(11), 1543–1553 (1997)

    Article  Google Scholar 

  15. Cho, J., Principe, J., Erdogmus, D., Motter, M.: Modeling and inverse controller design for an unmanned aerial vehicle based on the self-organizing map. IEEE Trans. Neural Netw. 17(2), 445–460 (2006)

    Article  Google Scholar 

  16. Chowdhary, G., Jategaonkar, R.: Aerodynamic parameter estimation from flight data applying extended and unscented kalman filter. In: Proceedings of the 2006 AIAA Atmospheric Flight Mechanics Conference (2006)

  17. Chowdhary, G., Lorenz, S.: Control of a vtol uav via online parameter estimation. In: Proceeding of the AIAA Guidance, Navigation, and Control Conference and Exhibit, pp. 1–15 (2005)

  18. Cory, R., Tedrake, R.: Experiments in fixed-wing uav perching. In: Proceedings of the AIAA Guidance, Navigation, and Control Conference (2008)

  19. Crassidis, J.L., Junkins, J.L.: Optimal Estimation of Dynamic Systems, 2nd edn. CRC Press, Boca Raton (2011)

    Google Scholar 

  20. Debusk, W.M., Chowdhary, G., Johnson, E.N.: Real-time system identification of a small multi-engine aircraft. In: Proceedings of the AIAA Atmospheric Flight Mechanics Conference, pp. 1–15 (2009)

  21. Derafa, L., Madani, T., Benallegue, A.: Dynamic modelling and experimental identification of four rotors helicopter parameters. In: Proceedings of the IEEE International Conference on Industrial Technology, pp. 1834–1839 (2006)

  22. Dongwon, J., Panagiotis, T.: Modeling and hardware-in-the-loop simulation for a small unmanned aerial vehicle. In: Proceedings of the 2007 AIAA Conference (2007)

  23. Dorobantu, A., Murch, A., Mettler, B., Balas, G.: Frequency domain system identification for a small, low-cost, fixed-wing uav. In: Proceedings of the 2011 AIAA Guidance, Navigation, and Control Conference (2011)

  24. Elfes, A., Montgomery, J., Hall, J., Joshi, S., Payne, J., Bergh, C.: Autonomous flight control for a titan exploration aerobot. In: Proceedings of the 8th International Symposium on Artificial Intelligence, Robotics and Automation in Space (2005)

  25. Engel, A., Teichert, B.: The photogrammetric potential of low-cost uavs in forestry and agriculture. Int. Arch. Photogramm. Remote. Sens. Spat. Inf. Sci. 1(B1), 1207–1214 (2008)

    Google Scholar 

  26. Evans, J., Elkaim, G., Parkinson, B.: System identification of an autonomous aircraft using gps. In: Proceedings of the ION Global Positioning System Conference, pp. 1065–1071 (1997)

  27. Farrell, J.A., Polycarpou, M.M.: Adaptive Approximation Based Control: Unifying Neural, Fuzzy and Traditional Adaptive Approximation Approaches, 1st edn. Wiley, Hoboken (2006)

    Book  Google Scholar 

  28. Finio, B.M., Perez-Arancibia, N.O., Wood, R.J.: System identification and linear time-invariant modeling of an insect-sized flapping-wing micro air vehicle. In: Proceedings of the 2011 IEEERSJ International Conference on Intelligent Robots and Systems, pp. 1107–1114 (2011)

  29. Garratt, M., Ahmed, B., Pota, H.: Platform enhancements and system identification for control of an unmanned helicopter. In: Proceedings of the 9th International Conference on Control Automation Robotics and Vision (2006)

  30. Hansen, S., Blanke, M.: Control surface fault diagnosis with specified detection probability - real event experiences. In: Proceedings of the 2013 Internatinal Conference on Unmanned Aircraft Systems, pp. 526–531 (2013)

  31. Hashimoto, S., Ogawa, T., Adachi, S., Tan, A., Miyamori, G.: System identification experiments on a large-scale unmanned helicopter for autonomous flight. In: Proceedings of the 2000 IEEE International Conference on Control Applications Conference, pp. 850–855 (2000)

  32. Heredia, G., Ollero, A.: Sensor fault detection in small autonomous helicopters using observer/kalman filter identification. In: Proceedings of the 2009 IEEE International Conference on Mechatronics, pp. 1–6 (2009)

  33. Hoburg, W., Tedrake, R.: System identification of post stall aerodynamics for uav perching. In: Proceedings of the AIAA Infotech@ Aerospace Conference, pp. 1–9 (2009)

  34. Hoffer, N.V., Coopmans, C., Jensen, A.M., Chen, Y.: Small low-cost unmanned aerial vehicle system identification: a survey and categorization. In: Proceeding of the International Conference on Unmanned Aircraft Systems, pp. 897–904 (2013)

  35. Hu, C., Huang, X., Hu, J., Zhu, J.: System identification of a small uav’s speeding up process before take-off. In: Proceedings of the 2004 5th Asian Cotnrol Conference. O’Reilly (2004)

  36. Isermann, R., Münchhof, M.: Identification of Dynamic Systems: An Introduction with Applications. Springer, New York (2010)

    Google Scholar 

  37. Ivler, C., Tischler, M.: System identification modeling for flight control design. In: Proceedings of the RAeS Rotorcraft Handling-Qualities Conference, vol. 47, p. 50 (2008)

  38. Jensen, A., Hardy, T., Mckee, M., Chen, Y.Q.: Using a multispectral autonomous unmanned aerial remote sensing platform (aggieair) for riparian and wetlands applications. In: Proceedings of the 2011 IEEE International Geoscience and Remote Sensing Symposium, pp. 3413–3416 (2011)

  39. Jung, D., Levy, E., Zhou, D., Fink, R., Moshe, J., Earl, A., Tsiotras, P.: Design and development of a low-cost test-bed for undergraduate education in uavs. In: Proceedings of the 44th IEEE Conference on Decision and Control, pp. 2739–2744 (2005)

  40. Kallapur, A., Ali, S., Anavatti, S.: Application of extended kalman filter towards uav identification. Auton. Robots Agents 207, 199–207 (2007)

    Article  Google Scholar 

  41. Kallapur, A., Samal, M., Vishwas, P., Sreenatha, A., Garratt, M.: A ukf-nn framework for system identification of small unmanned aerial vehicles. In: Proceedings of the 2008 11th International IEEE Conference on Intelligent Transportation Systems, pp. 1021–1026 (2008)

  42. Keesman, K.J.: System Identification: An Introduction. Springer, London (2011)

    Book  Google Scholar 

  43. Kim, B., Chang, Y., Lee, M.: System identification and 6-dof hovering controller design of unmanned model helicopter. JSME Int. J. Ser. C 49(4), 1048–1057 (2006)

    Article  Google Scholar 

  44. Kim, H., Shim, D.: A flight control system for aerial robots: algorithms and experiments. Control. Eng. Pract. 11(12), 1389–1400 (2003)

    Article  Google Scholar 

  45. Ko, J., Klein, D., Fox, D., Haehnel, D.: Gaussian processes and reinforcement learning for identification and control of an autonomous blimp. In: Proceedings of the 2007 IEEE International Conference on Robotics and Automation, pp. 10–14 (2007)

  46. Koos, S., Mouret, J., Doncieux, S.: Automatic system identification based on coevolution of models and tests. In: Proceedings of the 2009 IEEE Congress on Evolutionary Computation, pp. 560–567 (2009)

  47. Lei, X., Du, Y.: A linear domain system identification for small unmanned aerial rotorcraft based on adaptive genetic algorithm. J. Bionic Eng. 7(2), 142–149 (2010)

    Article  Google Scholar 

  48. Li, Z., Hoffer, N., Stark, B., Chen, Y.: Design, modeling and validation of a t-tail unmanned aerial vehicle. J. Intell. Robot. Syst. 69(1–4), 91–107 (2012)

    Google Scholar 

  49. Liu, M., Egan, G., Ge, Y.: Identification of attitude flight dynamics for an unconventional uav. In: Proceedings of the 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3243–3248 (2006)

  50. Ljung, L.: System Identification-Theory for User, 2nd edn. Prentice Hall, Upper Saddle River (1999)

    Google Scholar 

  51. Luo, Y., Chao, H., Di, L., Chen, Y.: Lateral directional fractional order (pi) control of a small fixed-wing unmanned aerial vehicles: controller designs and flight tests. IET Control Theory Appl. 5(18), 2156 (2011)

    Article  MathSciNet  Google Scholar 

  52. Manai, M., Desbiens, A., Gagnon, E.: Identification of a uav and design of a hardware-in-the-loop system for nonlinear control purposes. In: AIAA Guidance, Navigation, and Control Conference, vol. 8, pp. 6441–6446 (2005)

  53. Mettler, B., Tischler, M.B., Kanade, T.: System identification of small-size unmanned helicopter dynamics. In: Annual Forum Proceedings of the American Helicopter Society, vol. 2, pp. 1706–1717 (1999)

  54. Mettler, B., Kanade, T., Tischler, M.: System Identification Modeling of a Model-Scale Helicopter, pp. 1–25. Carnegie Mellon University, The Robotics Institute (2000)

  55. Morris, J., Van Nieuwstadt, M., Bendotti, P.: Identification and control of a model helicopter in hover. In: Proceedings of the American Control Conference, pp. 1238–1242 (1994)

  56. Morris, S., Holden, M.: Design of micro air vehicles and flight test validation. In: Proceedings of the Fixed, Flapping and Rotary Wing Vehicles at Very Low Reynolds Numbers Conference, pp. 153–176 (2000)

  57. Muhammad, H., Thien, H.P., Mulyanto, T.: Total leasts squares estimation of aerodynamic parameter of micro coaxial helicopter from flight data. Int. J. Basic Appl. Sci. 12(2), 44–52 (2012)

    Google Scholar 

  58. Ng, A., Coates, A., Diel, M., Ganapathi, V., Schulte, J., Tse, B., Berger, E., Liang, E.: Autonomous inverted helicopter flight via reinforcement learning. Exp. Robot. IX, 363–372 (2006)

    Google Scholar 

  59. Nino, J., Mitrache, F., Cosyn, P., Dekeyser, R.: Model identification of a micro air vehicle. J. Bionic Eng. 4(4), 227–236 (2007)

    Article  Google Scholar 

  60. Nong, Y., Qi, Z., Lin, D.: System identification of a small unmanned aerial vehicle based on time and frequency domain technologies. In: Proceedings of the 2011 9th World Congress on Intelligent Control and Automation, pp. 711–718 (2011)

  61. Park, W.J., Kim, E.T., Song, Y.K., Ko, B.J.: A study on the real-time parameter estimation of durumi-ii for control surface fault using flight test data (longitudinal motion). Int. J. Control. Autom. Syst. 5(4), 410–418 (2007)

    Google Scholar 

  62. Paw, Y.C., Balas, G.J.: Development and application of an integrated framework for small uav flight control development. Mechatronics 21(5), 789–802 (2011)

    Article  Google Scholar 

  63. Phang, S., Cai, C., Chen, B., Lee, T.: Design and mathematical modeling of a 4-standard-propeller (4sp) quadrotor. In: Prodeedings of the 10th World Congress on Intelligent Control and Automation, pp. 3270–3275 (2012)

  64. Phillips, W.F.: Mechanics of Flight, 2nd edn. Wiley, New York (2009)

    Google Scholar 

  65. Pounds, P., Mahony, R., Corke, P.: System identification and control of an aerobot drive system. In: Proceedings of the 2007 Information, Decision and Control Conference, pp. 154–159 (2007)

  66. Putro, I., Budiyono, A., Yoon, K., Kim, D.: Modeling of unmanned small scale rotorcraft based on neural network identification. In: Proceedings of the 2008 IEEE International Conference on Robotics and Biomimetics, pp. 1938–1943 (2009)

  67. Puttige, V., Anavatti, S.: Real-time neural network based online identification technique for a uav platform. In: Proceedings of the International Conference on Computational Intelligence for Modelling Control and Automation, and International Conference on Intelligent Agents, Web Tehcnologies and Internet Commerce (2006)

  68. Puttige, V., Anavatti, S.: Comparison of real-time online and offline neural network models for a uav. In: Proceedings of the International Joint Conference on Neural Networks, pp. 412–417 (2007)

  69. Puttige, V., Anavatti, S.: Real-time multi-network based identification with dynamic selection implemented for a low cost uav. In: Proceedings of the 2007 IEEE International Conference on Systems Man and Cybernetics, pp. 759–764 (2007)

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

    Google Scholar 

  71. Rango, A., Berte, A.L., Steele, C., Herrick, J.E., Bestelmeyer, B., Schmugge, T., Roanhorse, A., Jenkins, V.: Using unmanned aerial vehicles for rangelands: future potentials. Environ. Pract. 68, 159–168 (2006)

    Google Scholar 

  72. Rao, J.J., Gong, Z.B., Jiang, Z.: GA-based flight motion model parameter identification of a subminiature fixed-wing unmanned aerial vehicle. In: Proceedings of the 2007 IEEE International Conference on Mechatronics and Automation, pp. 3549–3554 (2007)

  73. Rimal, B., Putro, I., Budiyono, A., Min, D., Choi, E.: System identification of nn-based model reference control of ruav during hover. In: Artificial Neural Networks - Industrial and Control Engineering Applications, InTech, pp. 395–420 (2011)

  74. Salman, S., Puttige, V., Anavatti, S.: Real-time validation and comparison of fuzzy identification and state-space identification for a uav platform. In: Proceedings of the 2006 IEEE International Conference on Control Applications, pp. 2138–2143 (2006)

  75. Salman, S., Sreenatha, A., Choi, J.: Attitude dynamics identification of unmanned aircraft vehicle. Int. J. Control. Autom. Syst. 4(6), 782–787 (2006)

    Google Scholar 

  76. Samal, M., Anavatti, S., Garratt, M.: Neural network based system identification for autonomous flight of an eagle helicopter. In: Proceedings of the 17th World Congress of the International Federation of Automatic Control, pp. 7421–7426 (2008)

  77. Samal, M., Anavatti, S., Garratt, M.: Real-time neural network based identification of a rotary-wing uav dynamics for autonomous flight. In: Proceedings of the 2009 IEEE International Conference on Industrial Technology. IEEE (2009)

  78. Schafroth, D., Bermes, C., Bouabdallah, S., Seigwart, R.: Modeling and system identification of the mufly micro helicopter. J. Intell. Robot. Syst. 57(1–4), 27–47 (2010)

    Article  MATH  Google Scholar 

  79. Schafroth, D., Bermes, C., Bouabdallah, S., Siegwart, R.: Modeling, system identification and robust control of a coaxial micro helicopter. Control. Eng. Pract. 18(7), 700–711 (2010)

    Article  Google Scholar 

  80. Shim, D.H., Kim, H., Sastry, S.: Control system design for rotorcraft-based unmanned aerial vehicles using time-domain system identification. In: Proceedings of the 2000 IEEE International Conference on Control Applications Conference Proceedings, vol. 2000(2), pp. 808–813 (2000)

  81. Teo, R., Jang, J., Tomlin, C.: Automated multiple uav flight-the stanford dragonfly uav program. In: Proceedings of the 43rd IEEE Conference on Decision and Control, vol. 4, pp. 4268–4273 (2004)

  82. Tischler, M.B., Remple, R.K.: Aircraft and Rotorcraft System Identification: Engineering Methods with Flight Test Examples. AIAA, Inc., Virginia (2006)

    Google Scholar 

  83. Vélez, C., Agudelo, A., Alvarez, J.: Modeling, simulation and rapid prototyping of an unmanned mini-helicopter. In: Proceedings of the AIAA Modeling and Simulation Technologies Conference (2006)

  84. Wu, H., Sun, D., Zhou, Z.: Model identification of a micro air vehicle in loitering flight based on attitude performance evaluation. IEEE Trans. Robot. 20(4), 702–712 (2004)

    Article  Google Scholar 

  85. Yuan, W., Katupitiya, J.: A time-domain grey-box system identification procedure for scale model helicopters. In: Proceedings of the 2011 Australasian Conference on Robotics and Automation (2011)

Download references

Author information

Authors and Affiliations

  1. Center for Self-Organizing and Intelligent Systems (CSOIS), Utah State University, Logan, UT, USA

    Nathan V. Hoffer & Calvin Coopmans

  2. AggieAir Flying Circus (AAFC), Utah Water Research Laboratory, Utah State University, Logan, UT, USA

    Austin M. Jensen

  3. Mechatronics, Embedded Systems and Automation (MESA) Lab, University of California, Merced, Merced, CA, USA

    YangQuan Chen

Authors
  1. Nathan V. Hoffer
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Calvin Coopmans
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Austin M. Jensen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. YangQuan Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nathan V. Hoffer.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Hoffer, N.V., Coopmans, C., Jensen, A.M. et al. A Survey and Categorization of Small Low-Cost Unmanned Aerial Vehicle System Identification. J Intell Robot Syst 74, 129–145 (2014). https://doi.org/10.1007/s10846-013-9931-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10846-013-9931-6

Keywords

Search

Navigation