A novel dynamic identification model for small unmanned helicopters

A novel dynamic identification model for small unmanned helicopters

Jian ZhouMin Wang 

School of Electronics and Information, Xi’an Polytechnic University, Xi’an 710048, China

Corresponding Author Email: 
zhoujian0627@sohu.com
Page: 
379-390
|
DOI: 
https://doi.org/10.3166/JESA.50.379-390
| |
Published: 
31 December 2017
| Citation

OPEN ACCESS

Abstract: 

Targeting small unmanned helicopters with complex dynamic features, this paper puts forward a dynamic modelling method based on frequency domain identification. Firstly, the angular dynamic model structure of the small unmanned helicopter was determined by mechanism modeling, and the modelling parameters were obtained through frequency identification of partial coherence analysis. After that, the system model was constructed in light of the model structure and modelling parameters. The proposed model was then subjected to a flight test, and compared against time-domain data. The results show that the model demonstrated the dynamic features of the target helicopter with high accuracy and adaptability. The research findings provide new insights into the detection of dynamic features of small aircrafts.

Keywords: 

small unmanned helicopter, frequency domain identification, dynamic modeling, time domain verification

1. Introduction
2. Small unmanned helicopter system
3. Dynamic mechanism modeling
4. Dynamic model parameter identification
5. Identification parameter time-domain verification
6. Conclusion
Acknowledgment

The authors would like to thank the whole automation team for their invaluable assistance and support. In particular, Wang Min and Hong Liang. The Scientific Research Program Funded by Shaanxi Provincial Education Department under Grant No.17JK0345, Doctoral startup fund of Xi'an Polytechnic University under No.BS1415.

  References

Adiprawita W., Ahmad A.S., Semibiring J. (2007). Automated flight test and system identification for rotary wing small aerial platform using frequency responses analysis. ICIUS, Vol. 4, No. 4, pp. 50-56. https://doi.org/10.1016/s1672-6529(07)60037-7

Bendat J. S., Piersol A. G. (1990). Random, data: Analysis and measurement procedures. Wiley.

Budiyono A., Yoon K. J., Daniel F. D. (2009). Integrated identification modeling of rotorcraft-based unmanned aerial vehicle. 17th Mediterranean Conference on Control & Automation, Vol. 10, pp. 898-903. https://doi.org/10.1109/MED.2009.5164659

Castillo P., Lozano R., Dzul A. E. (2005). Modeling and control of mini-flying machines. New York Springer, Vol. 26, No. 3, pp. 122-124. https://doi.org/10.1007/1-84628-179-2

Leishman J. G. (2000). Principles of helicopter aerodynamics. Cambridge University Press, Vol. 5, pp. 78.

Liceaga-Castro J., Verde C., O’Reilly J., Leithead W. E. (1995). Helicopter flight control using individual channel design. IEE Proceedings of Control Theory and Application, Vol. 142, No. 1, pp. 58-72. https://doi.org/10.1049/ip-cta:19951575

Ljung L. (1993). Some results on identifying linear system using frequency domain data. 32nd IEEE Conference on Decision and Control, Vol. 4, pp. 3534-3538. https://doi.org/10.1109/CDC.1993.325876

Tischler M. B. (1987). Frequency-response identification of XV-15 tilt-rotor aircraft dynamics. NASA Technical Report.

Tischler M. B., Remple R. K. (2006). Aircraft and rotorcraft system identification: engineering methods with flight-test example. American Institute of Aeronautics and Astronautics.

Zhou J., Hong L., Li W. C. (2014). Design and implementation of small-scale single rotor. Journal of Xi'an Polytechnic University, Vol. 28, No. 5, pp. 626-630.