한국항공우주학회지 (Journal of the Korean Society for Aeronautical & Space Sciences)

  • 제48권9호
  • /
  • Pages.691-701
  • /
  • 2020
  • /
  • 1225-1348(pISSN)
  • /
  • 2287-6871(eISSN)
한국항공우주학회 (The Korean Society for Aeronautical and Space Sciences)
권호 표지
DOI QR코드

DOI QR Code

컨벡스 최적화 기법 기반 전기추진 수직이착륙 무인기의 추진 시스템 고장 대처를 위한 회전익 모드 믹서 설계

Actuator Mixer Design in Rotary-Wing Mode Based on Convex Optimization Technique for Electric VTOL UAV

  • 투고 : 2020.05.07
  • 심사 : 2020.08.04
  • 발행 : 2020.09.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

본 논문은 전기 추진 수직이착륙 무인기의 형상에 적합하도록 회전익 모드에서 추진시스템이 고장난 상황을 대처하기 위한 컨벡스 최적화 기법 기반의 믹서를 제시한다. 각 모터 및 프로펠러의 고장 영향성을 분석하기 위하여 컨벡스 함수 성질을 이용하여 가용 조종력 집합을 계산하였으며 이를 도시하는 방법을 기술하였다. 조종력 할당을 최적화 문제로 정의하고, 실시간으로 최적해를 구하기 위하여 컨벡스 함수로 문제를 재정의하였다. 컨벡스 최적화 솔버를 사용하여 수직 이착륙 비행 모드의 믹서를 구현하였으며 조종력 할당 기법들의 성능을 가용 조종력 범위 집합으로 비교하였다. 최종적으로 비선형 6자유도 시뮬레이션을 통하여 타기법(의사역행렬 기법, 재분배의사역행렬 기법)과 비교 분석하였다.

An actuator mixer design using convex optimization technique situation where the propulsion system of an electric VTOL UAV during vertical take-off and landing maneuvers is proposed. The attainable control set to analyze the impact from failure of each motor and propeller can be calculated and illustrated using the properties of the convex function. The control allocation can be defined as a convex function optimization problem to obtain an optimal solution in real time. The mixer is implemented using a convex optimization solver, and the performance of the control allocation methods is compared to the attainable control set. Finally, the proposed mixer is compared with other techniques with nonlinear sux degree-of-freedom simulation.

키워드

참고문헌

  1. Whittle, R., "Air Mobility Bonanza Beckons Eletric VTOL Developers," Vertiflite, March-April 2017, pp. 14-21.
  2. Marks, A., Whidborne, J. F. and Yamamoto, I., "Control Allocation for Fault Tolerant Control of a VTOL Octorotor," UKACC International Conference on Control, Cardiff, September 3-5, 2012, pp. 357-362.
  3. Alwi, H. and Edwards, C., "Fault Tolerant Control of an Octorotor Using LPV based Sliding Mode Control Allocation," American Control Conference (ACC), Washington, DC, USA, June 17-19, 2013, pp. 6505-6510.
  4. Merheb, A., Nourra, H. and Bateman, F., "Active Fault Tolerant Control of Octorotor UAV using Dynamic Control Allocation," International Conference on Intelligent Unmanned Systems, Montreal, Quebec, Canada, September 29-October 1, 2014.
  5. Du, G., Quan, Q. and Cai, K., "Controllability Analysis and Degraded Control for a Class of Hexacopters Subject to Rotor Failures," Journal of Intelligent Robotic Systems, September 2014.
  6. Horn, R. A. and Johnson, C. R., Matrix Analysis, Cambridge University Press, 1985.
  7. Oppenheimer, M. W., Doman, D. B. and Sighthorsson, D. O., "Dynamics and Control of a Hovering Biometic Vehicle using Biased Wingbeat Forcing Function," Journal of Guidance, Navigation and Control, Vol. 34, 2011, pp. 204-217 https://doi.org/10.2514/1.49735
  8. Shi, J., Zhang, W. and Liu, X., "Research on Allocation Efficiency of the Redistributed Pseudo Inverse Algorithm," Science China Information Sciences, Vol. 53, 2010, pp. 271-277. https://doi.org/10.1007/s11432-010-0032-x
  9. Frangenberg, M., Stephan, J. and Fichter, W., "Fast Actuator Fault Detection and Reconfiguration for Multicopters," AIAA Guidance, Navigation, and Control Conference, Florida, USA, January, 2015.
  10. Schneider, T., Ducard, G., Rudin, K. and Strupler, P., "Fault-tolerant Control Allocation for Mulirotor Helicopters using Parametric Programming," International Micro Air Vehicle Conference and Flight Competition, Braunschweig, Germany, July, 2012.
  11. Boyd, S. and Vandenberghe, L., "Convex optimization," Cambridge University Press, Cambridge, 2004.
  12. Nesterov, Y. and Nemirovskii, A., "Interior point polynomial algorithms in convex programming," SIAM, Vol. 13, Philadelphia, 1994.
  13. Ye, Y., Interior point algorithms: theory and analysis, Wiley, New York, 1997.
  14. Nocedal, J. and Wright, S. J., Numerical optimization Springer, Berlin, 1999.
  15. Grant, M. and Boyd, S., "CVX: Matlab software for disciplined convex programming (web page and software)," http://www.stanford.edu/-boyd/cvx/, July 2008.
  16. Lofberg, J., "YALMIP: a toolbox for modeling and optimization in MATLAB," In Proceedings of the CACSD conference, Taipei, Taiwan, http://control.ee.ethz.ch/-joloef/yalmip.php, 2004.
  17. Chu, E., Parikh, N., Domahidi, A. and Boyd, S., "Code generation for embedded secondorder cone programming," Proceedings of the European Control Conference, 2013, pp. 1547-1552,
  18. O'Donoghue, B., Chu, E., Parikh, N. and Boyd, S., "Conic optimization via operator splitting and homogeneous self-dual embedding," Journal of Optimization Theory and Applications, 2016, pp. 1-27.
  19. Udell, M., Mohan, K., Zeng, D., Hong, J., Diamond, S. and Boyd, S., "Convex optimization in Julia," In Proceedings of the Workshop for High Performance Technical Computing in Dynamic Languages, 2014, pp. 18-28.
  20. Diamond, S. and Boyd, S., "CVXPY: A Python-Embedded Modeling Language for Convex Optimization," Journal of Machine Learning Research, Vol. 17, 2016, pp. 1-5.
  21. Dyurham, W. C., "Constrained Control Allocation," Journal Guidance, Control and Dynamics, Vol. 16, 1993, pp. 717-725. https://doi.org/10.2514/3.21072
  22. Petersen, J. A. M. and Bodson, M., "Constrained Quadratic Programming Techniques for Control Allocation," IEEE Transactions on Control Systems Technology, Vol. 14, pp. 91-98. https://doi.org/10.1109/TCST.2005.860516
  23. Matt, J., "Analyze N-dimensional Polyhedra in terms of Vertices or (In)Equalities," MATLABCENTRAL.
  24. Mattingleym, J. and Boyd, S., "CVXGEN: a Code Generator for Embedded Convex Optimization," Optimization and Engineering, Vol. 13, No. 1, 2012, pp. 1-27. https://doi.org/10.1007/s11081-011-9176-9