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Dual–Authority Thrust–Vectoring of a Tri–TiltRotor employing Model Predictive Control

Abstract

This paper addresses the exploitation of the combined potential of the directly-actuated and the underactuated control authorities of unmanned aerial vehicles with thrust-vectoring actuation. For the modeling, control synthesis and experimental evaluation a custom developed unmanned tri-tiltrotor is employed, equipped with rotor-tilting mechanisms which enable the direct actuation of its longitudinal dynamics, while retaining the standard body-pitching underactuated authority. An explicit model predictive control scheme relying on constrained multiparametric optimization is proposed for the dual-authority optimal control. The backbone of this scheme is a modeling representation that incorporates the separate internal dynamics of the two actuation principles and their interferences as they concurrently act on the free-flying vehicle body, while tractably representing their differentiated effects on the evolution of the longitudinal dynamics. This paper additionally presents the key implemented features that enable the autonomous operation of the employed tilt-rotor platform, in order to provide a reliable testbed for experimental evaluation. Finally, extensive experimental studies which conclusively validate this strategy’s increased efficiency are demonstrated.

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References

  1. 1.

    Wenzel, K., Rosset, P., Zell, A.: Low-cost visual tracking of a landing place and hovering flight control with a microcontroller. J. Intell. Robot. Syst. 57, 297–311 (2010)

    Article  MATH  Google Scholar 

  2. 2.

    Alexis, K., Nikolakopoulos, G., Tzes, A.: Model predictive quadrotor control: attitude, altitude and position experimental studies. IET Control Theory Appl. 6(12), 1812–1827 (2012)

    Article  MathSciNet  Google Scholar 

  3. 3.

    Burri, M., Nikolic, J., Hürzeler, C., Rehder, J., Siegwart, R.: Aerial service robots for visual inspection of thermal power plant boiler systems. In: Proceedings of the 2nd International Conference on Applied Robotics for the Power Industry (2012)

  4. 4.

    Doherty, P., Rudol, P.: A uav search and rescue scenario with human body detection and geolocalization. In: AI 2007: Advances in Artificial Intelligence, pp. 1–13. Springer (2007)

  5. 5.

    Orsag, M., Korpela, C., Oh, P.: Modeling and control of mm-uav: Mobile manipulating unmanned aerial vehicle. J. Intell. Robot. Syst. 69(1-4), 227–240 (2013)

    Article  Google Scholar 

  6. 6.

    Justin, T., Joe, P., Koushil, S., Vijay, K.: Avian-inspired grasping for quadrotor micro uavs. In: IDETC/CIE ASME (2013)

  7. 7.

    Pounds, P., Bersak, D.R., Dollar, A.M.: The yale aerial manipulator: Grasping in flight. In: 2011 IEEE International Conference on Robotics and Automation (ICRA), pp. 2974–2975 (2011)

  8. 8.

    Mellinger, D., Shomin, M., Michael, N., Kumar, V.: Cooperative grasping and transport using multiple quadrotors, vol. 83, pp. 545–558. Springer, Berlin Heidelberg (2013)

    Google Scholar 

  9. 9.

    Marconi, L., Naldi, R., Gentili, L.: Modelling and control of a flying robot interacting with the environment. Automatica 47(12), 2571–2583 (2011). http://www.sciencedirect.com/science/article/pii/S0005109811004602

    Article  MathSciNet  MATH  Google Scholar 

  10. 10.

    Alexis, K., Huerzeler, C., Siegwart, R.: Hybrid modeling and control of a coaxial unmanned rotorcraft interacting with its environment through contact, pp. 5397–5404, Karlsruhe (2013)

  11. 11.

    Ryll, M., Bulthoff, H., Giordano, P.R.: Modeling and control of a quadrotor uav with tilting propellers. In: 2012 IEEE International Conference on Robotics and Automation (ICRA). IEEE, pp. 4606–4613 (2012)

  12. 12.

    Falconi, R., Melchiorri, C.: Dynamic model and control of an over-actuated quadrotor uav. In: 10th IFAC Symposium on Robot Control, vol. 10 no. 1, pp. 192–197 (2012)

  13. 13.

    Long, Y., Wang, L., Cappelleri, D.J.: Modeling and global trajectory tracking control for an over-actuated mav. Adv. Robot. 28(3), 2014

  14. 14.

    Papachristos, C., Alexis, K., Tzes A.: Design and experimental attitude control of an unmanned tilt–rotor aerial vehicle. In: 15th International Conference on Advanced Robotics, Tallin (2011)

  15. 15.

    Cetinsoy, E., Dikyar, S. , Hancer, C., Oner, K., Sirimoglu, E., Unel, M., Aksit, M.: Design and construction of a novel quad tilt-wing {UAV}. Mechatronics 6, 723–745 (2012). special Issue on Intelligent Mechatronics (LSMS2010 & ICSEE2010). http://www.sciencedirect.com/science/article/pii/S095741581200044X

    Article  Google Scholar 

  16. 16.

    Ryll, M., Bulthoff, H.H., Giordano, P.R.: First flight tests for a quadrotor uav with tilting propellers. In: 2013 IEEE International Conference on Robotics and Automation (ICRA), pp. 295–302. IEEE (2013)

  17. 17.

    Papachristos, C., Alexis, K., Tzes A.: Technical activities execution with a tiltrotor uas employing explicit model predictive control. In: 2014 World Congress of the International Federation of Automatic Control (IFAC), pp. 11036–11042, Cape Town (2014)

  18. 18.

    Papachristos C., Tzes A.: Large object pushing via a direct longitudinally-actuated unmanned tritiltrotor. In: 2013 Mediterranean Conference on Control Automation (MED), pp. 173–178 (2013)

  19. 19.

    Papachristos, C., Alexis, K., Tzes, A.: Efficient force exertion for aerial robotic manipulation: Exploiting the thrust-vectoring authority of a tri-tiltrotor uav. In: 2014 International Conference on Robotics and Automation (ICRA), pp. 4500–4505, Hong Kong (2014)

  20. 20.

    Papachristos, C., Alexis, K., Tzes, A.: Towards a high–end unmanned tri–tiltrotor: Design, modeling and hover control. In: Mediterranean Conference on Control Automation (MED), pp. 1579–1584 (2012)

  21. 21.

    Papachristos, C., Alexis, K., Tzes, A.: Model predictive hovering–translation control of an unmanned tri–tiltrotor. In: 2013 International Conference on Robotics and Automation (ICRA), pp. 5405–5412, Karlsruhe (2013)

  22. 22.

    The MathWorks Inc.: Aerospace Blockset 3 User’s Guide (2009)

  23. 23.

    Dickeson, J., Miles, D., Cifdaloz, O., Wells, V., Rodriguez, A.: Robust lpv h 8 gain-scheduled hover-to-cruise conversion for a tilt-wing rotorcraft in the presence of cg variations. In: 2007 46th IEEE Conference on Decision and Control, pp. 2773–2778 (2007)

  24. 24.

    Stevens, B.L., Lewis, F.L.: Aircraft Control and Simulation. Wiley Interscience (1992)

  25. 25.

    Ljung, L.: System Identification: Theory for the User, 2nd. Prentice Hall Inc., Upper Saddle River, NJ (1999)

    Google Scholar 

  26. 26.

    Hoffmann, G.M., Huang, H., Waslander, S.L., Tomlin, C.J.: Precision flight control for a multi-vehicle quadrotor helicopter testbed. Control. Eng. Pract. 19(9), 1023–1036 (2011)

    Article  Google Scholar 

  27. 27.

    Tischler, M.B.: Advances in aircraft flight control. CRC Press (1996)

  28. 28.

    Bouabdallah, S.: Design and control of quadrotors with application to autonomous flying. Ph.D. dissertation, École Polytechnique federale de Lausanne (2007)

  29. 29.

    Horn, B., Schunck, B.: Determining optical flow. Artif. Intell., 185–204 (1981)

  30. 30.

    Rosten, E., Drummond, T. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) Machine Learning for High-Speed Corner Detection ser. Lecture Notes in Computer Science, vol. 3951. Springer, Berlin Heidelberg (2006)

  31. 31.

    Baker S., Matthews, I.: Lucas-kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56, 221–255 (2004)

    Article  Google Scholar 

  32. 32.

    Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd. Cambridge University Press, Cambridge (2004)

    Book  MATH  Google Scholar 

  33. 33.

    Chen, Y., Wu, J., Cheung, C.N.: Lyapunovs stability theory-based model reference adaptive control for permanent magnet linear motor drives. In: 2004 1st International Conference on Power Electronics Systems and Applications proceedings: 9 to 11 November 2004, pp. 260–266, Hong Kong (2004)

  34. 34.

    Dhaouadi, R., Ghorbel, F., Gandhi, P.: Modeling and analysis of hysteresis in harmonic drive gears. In: Proceedings 16th IMACS World Congr, pp. 21–25 (2000)

  35. 35.

    Seidl, D.R., Lam, S.-L., Putman, J.A., Lorenz, R.D.: Neural network compensation of gear backlash hysteresis in position-controlled mechanisms. IEEE Trans. Ind. Appl. 31(6), 1995

  36. 36.

    The smith predictor Control of Dead-time Processes, ser. Advanced Textbooks in Control and Signal Processing. doi:10.1007/978-1-84628-829-65, pp. 131–163. Springer, London (2007)

  37. 37.

    Kvasnica, M., Grieder, P., Baotic, M., Morari, M.: Multi-Parametric Toolbox (MPT). Automatic Control Laboratory , Swiss Federal Institute of Techonology (ETH) (2004)

    Book  Google Scholar 

  38. 38.

    Loefberg, J.: Yalmip : A toolbox for modeling and optimization in MATLAB. In: Proceedings of the CACSD Conference. http://users.isy.liu.se/johanl/yalmip, Taipei (2004)

  39. 39.

    I. Gurobi Optimization, Gurobi optimizer reference manual, 2014. http://www.gurobi.com

  40. 40.

    Bemporad, A., Morari, M., Dua, V., Pistikopoulos, E.: The explicit linear quadratic regulator for constrained systems. Automatica 38(1), 3–20 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  41. 41.

    Baotic, M.: Optimal control of piecewise affine systems. Ph.D. dissertation, Swiss Federal Institute of Technology, Zurich (2005)

    Google Scholar 

  42. 42.

    Herceg, M., Kvasnica, M., Jones, C., Morari, M.: Multi-Parametric Toolbox 3.0. In: Proceedings of the European Control Conference, pp. 502–510, Zürich (2013)

  43. 43.

    Kvasnica, M.: Real–Time Model Predictive Control via Multi–Parametric Programming: Theory and Tools, VDM Verlag (2009)

  44. 44.

    Bemporad, A.: Modeling, Control, and Reachability Analysis of Discrete–Time Hybrid Systems. University of Sienna (2003)

  45. 45.

    Kvasnica, M., Rauova, I., Fikar, M.: Automatic code generation for real-time implementation of model predictive control. In: 2010 IEEE International Symposium on Computer-Aided Control System Design (CACSD), pp. 993–998 (2010)

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Correspondence to Christos Papachristos.

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Papachristos, C., Alexis, K. & Tzes, A. Dual–Authority Thrust–Vectoring of a Tri–TiltRotor employing Model Predictive Control. J Intell Robot Syst 81, 471–504 (2016). https://doi.org/10.1007/s10846-015-0231-1

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Keywords

  • Unmanned aerial vehicles
  • TiltRotor
  • Thrust vectoring
  • Dual authority
  • Model predictive control