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Synthesis and Flight Test of Automatic Landing Controller Using Quantitative Feedback Theory

Published Online:https://doi.org/10.2514/1.G001758

Landing is a challenging flight phase for automatic control of fixed-wing aircraft. For unmanned air vehicles in particular, it is imperative that model uncertainty be considered in the control synthesis. These vehicles tend to have limited sensors and instrumentation yet must achieve sufficient performance in the presence of modeling uncertainties and exogenous inputs such as turbulence. Quantitative feedback theory has been reported in the literature for design of automatic landing control laws, but none of these controllers has been flight-tested. In this paper, quantitative feedback theory is employed to synthesize robust discrete-time controllers for automatic landing of an unmanned air vehicle. A low-cost flight vehicle with standard aileron, rudder, elevator, and throttle controls is used. Dynamic simulation is conducted using uncertain aircraft models and sensor noise profiles derived from flight hardware. Controllers are initially synthesized in deterministic simulations. Control validation is performed using a Monte Carlo analysis of stochastic simulations. Sources of uncertainty considered are sensor noise, model uncertainty, and static winds. Landing-phase simulations presented in this paper indicate a routinely high probability of a successful landing in relatively calm wind conditions. The flight-testing process is discussed, and time histories from two automatic landings are presented. Dynamic responses in flight test are found to be similar to the simulation, but a significant amount of control redesign is still required to achieve adequate experimental performance. The methodology is judged to be a promising candidate for an automatic landing controller for unmanned air vehicles.

References

  • [1] Blakelock J. H., Automatic Control of Aircraft and Missiles, Vol. 6, Wiley, New York, 1991, pp. 81–98, 176–188. Google Scholar

  • [2] Barrows G. L., Chahl J. S. and Srinivasan M. V., “Biomimetic Visual Sensing and Flight Control,” Proceedings of the Bristol UAV Conference, Dept. of Aerospace Engineering, Univ. of Bristol, Bristol, 2002, pp. 159–168. Google Scholar

  • [3] Kingston D. B. and Beard R. W., “Real-Time Attitude and Position Estimation for Small UAVs Using Low-Cost Sensors,” AIAA 3rd “Unmanned Unlimited” Technical Conference, Workshop and Exhibit, AIAA Paper  2004-6488, Sept. 2004. LinkGoogle Scholar

  • [4] Kim J.-H., Sukkarieh S. and Wishart S., “Real-Time Navigation, Guidance, and Control of a UAV Using Low-Cost Sensors,” Field and Service Robotics, Springer, Berlin, 2006, pp. 299–309. CrossrefGoogle Scholar

  • [5] Barber D. B., Griffiths S. R., McLain T. W. and Beard R. W., “Autonomous Landing of Miniature Aerial Vehicles,” Journal of Aerospace Computing, Information, and Communication, Vol. 4, No. 5, 2007, pp. 770–784. doi:https://doi.org/10.2514/1.26502 LinkGoogle Scholar

  • [6] Barber B., McLain T. and Edwards B., “Vision-Based Landing of Fixed-Wing Miniature Air Vehicles,” Journal of Aerospace Computing, Information, and Communication, Vol. 6, No. 3, 2009, pp. 207–226. doi:https://doi.org/10.2514/1.36201 LinkGoogle Scholar

  • [7] Roos J.-C. and Peddle I., “Autonomous Take-Off and Landing of a Low Cost Unmanned Aerial Vehicle,” R & D Journal of the South African Institution of Mechanical Engineering, Vol. 25, South African Inst. of Mechanical Engineering, Stellenbosch, South Africa, 2009. Google Scholar

  • [8] Huh S. and Shim D. H., “A Vision-Based Automatic Landing Method for Fixed-Wing UAVs,” Journal of Intelligent and Robotic Systems, Vol. 57, No. 1, Jan. 2010, pp. 217–231. CrossrefGoogle Scholar

  • [9] Laiacker M., Kondak K., Schwarzbach M. and Muskardin T., “Vision Aided Automatic Landing System for Fixed Wing UAV,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE Publ., Piscataway, NJ, 2013, pp. 2971–2976. Google Scholar

  • [10] Thurrowgood S., Moore R. J., Soccol D., Knight M. and Srinivasan M. V., “A Biologically Inspired, Vision-Based Guidance System for Automatic Landing of a Fixed-Wing Aircraft,” Journal of Field Robotics, Vol. 31, No. 4, 2014, pp. 699–727. doi:https://doi.org/10.1002/rob.2014.31.issue-4 CrossrefGoogle Scholar

  • [11] Senpheng M. and Ruchanurucks M., “Automatic Landing Assistant System Based on Stripe Lines on Runway Using Computer Vision,” Proceedings of the IEEE International Conference on Science and Technology, IEEE Publ., Piscataway, NJ, 2015, pp. 35–39. Google Scholar

  • [12] Fan Z. and Yong W., “Automatic Landing of Unmanned Aerial Vehicle Using Fuzzy Control,” Proceedings of the IEEE International Conference on Information and Automation, IEEE Publ., Piscataway, NJ, 2013, pp. 472–477. Google Scholar

  • [13] Nho K. and Agarwal R. K., “Automatic Landing System Design Using Fuzzy Logic,” Journal of Guidance, Control, and Dynamics, Vol. 23, No. 2, 2000, pp. 298–304. doi:https://doi.org/10.2514/2.4522 JGCODS 0731-5090 LinkGoogle Scholar

  • [14] Lungu R., Lungu M. and Grigorie L. T., “Automatic Control of Aircraft in Longitudinal Plane During Landing,” IEEE Transactions on Aerospace and Electronic Systems, Vol. 49, No. 2, 2013, pp. 1338–1350. doi:https://doi.org/10.1109/TAES.2013.6494418 IEARAX 0018-9251 CrossrefGoogle Scholar

  • [15] Joos A., Müller M., Baumgärtner D. and Allgöwer F., “Nonlinear Predictive Control Based on Time-Domain Simulation for Automatic Landing,” AIAA Guidance, Navigation, and Control Conference, AIAA Paper  2011-6298, Aug. 2011. LinkGoogle Scholar

  • [16] You D. I., Jung Y. D., Cho S. W., Shin H. M., Lee S. H. and Shim D. H., “A Guidance and Control Law Design for Precision Automatic Take-Off and Landing of Fixed-Wing UAVs,” AIAA Guidance, Navigation, and Control Conference, AIAA Paper  2012-4674, Aug. 2012. LinkGoogle Scholar

  • [17] Jianfeng Z. and Caijuan J., “Automatic Landing Controller Design and Simulation of Flying-Wing Unmanned Aerial Vehicle,” Proceedings of the IEEE 2nd International Conference on Measurement, Information, and Control, IEEE Publ., Piscataway, NJ, 2013, pp. 893–896. Google Scholar

  • [18] Juang J.-G., Chien L.-H. and Lin F., “Automatic Landing Control System Design Using Adaptive Neural Network and its Hardware Realization,” IEEE Systems Journal, Vol. 5, No. 2, 2011, pp. 266–277. doi:https://doi.org/10.1109/JSYST.2011.2134490 CrossrefGoogle Scholar

  • [19] Yaniv O., Quantitative Feedback Design of Linear and Nonlinear Control Systems, Vol. 509, Springer, Boston, MA, 1999, pp. 8–9, 15–56. CrossrefGoogle Scholar

  • [20] Sheldon S. N. and Rasmussen S. J., “Development and First Successful Flight Test of a QFT Flight Control System,” Proceedings of the IEEE National Aerospace and Electronics Conference, (NAECON’94), IEEE Publ., Piscataway, NJ, 1994, pp. 629–636. Google Scholar

  • [21] Keating M., Pachter M. and Houpis C., “QFT Applied to Fault Tolerant Flight Control System Design,” Proceedings of the American Control Conference, Vol. 1, IEEE Publ., Piscataway, NJ, 1995, pp. 184–188. Google Scholar

  • [22] Wu S.-F., Grimble M. J. and Wei W., “QFT Based Robust/Fault Tolerant Flight Control Design for a Remote Pilotless Vehicle,” Proceedings of the IEEE International Conference on Control Applications, Vol. 1, IEEE Publ., Piscataway, NJ, 1999, pp. 57–62. Google Scholar

  • [23] Houpis C. and Rasmussen S., “Unmanned Research Vehicle: Development, Implementation, and Flight Test of a MIMO Digital Flight Control System Designed Using Quantitative Feedback Theory,” Proceedings of the International Symposium on Quantitative Feedback Theory and Robust Frequency Domain Methods, Defense Technical Information Center, 1999, pp. 1–13. Google Scholar

  • [24] Santander A. and Aranda J., “QFT for the Design of an Aircraft Flight Control,” IFAC Proceedings Volumes, Vol. 38, No. 1, 2005, pp. 138–143. doi:https://doi.org/10.3182/20050703-6-CZ-1902.01984 CrossrefGoogle Scholar

  • [25] Schuck F., Heller M., Baier T. and Holzapfel F., “Longitudinal Robust Controller for Excellent Handling Qualities Design of a General Aviation Aircraft Using QFT,” AIAA Guidance, Navigation, and Control (GNC) Conference, AIAA Paper  2013-5180, Aug. 2013. LinkGoogle Scholar

  • [26] Doyle J. C., “Quantitative Feedback Theory (QFT) and Robust Control,” Proceedings of the American Control Conference, IEEE Publ., Piscataway, NJ, 1986, pp. 1691–1698. Google Scholar

  • [27] Kerr M. L., Lan C.-Y. and Jayasuriya S., “Non-Sequential MIMO QFT Control of the X-29 Aircraft Using a Generalized Formulation,” International Journal of Robust and Nonlinear Control, Vol. 17, Nos. 2–3, 2007, pp. 107–134. doi:https://doi.org/10.1002/rnc.1106 CrossrefGoogle Scholar

  • [28] Wagner T. and Valasek J., “Digital Autoland Control Laws Using Direct Digital Design and Quantitative Feedback Theory,” Journal of Guidance, Control, and Dynamics, Vol. 30, No. 5, 2007, pp. 1399–1413. doi:https://doi.org/10.2514/1.27761 JGCODS 0731-5090 LinkGoogle Scholar

  • [29] Woodbury T. D., “Synthesis and Hardware Implementation of an Unmanned Aerial Vehicle Automatic Landing System Utilizing Quantitative Feedback Theory,” M.S. Thesis, Dept. of Aerospace Engineering, Texas A&M Univ., College Station, TX, 2014. Google Scholar

  • [30] Chao H., Cao Y. and Chen Y., “Autopilots for Small Fixed-Wing Unmanned Air Vehicles: A Survey,” Proceedings of the International Conference on Mechatronics and Automation (ICMA 2007), IEEE Publ., Piscataway, NJ, 2007, pp. 3144–3149. Google Scholar

  • [31] Borghesani C., Chait Y. and Yaniv O., The QFT Frequency Domain Control Design Toolbox for Use with MATLAB: User’s Guide, Terasoft, July 2003, pp. 3-1–3-30, 5-3, 5-10–5-15, http://www.terasoft.com/qft/QFTManual.pdf. Google Scholar

  • [32] Roskam J., Airplane Flight Dynamics and Automatic Flight Controls, DARCorporation, Lawrence, KA, 2009, pp. 3–6, 65–67, 78. Google Scholar

  • [33] X-Plane Operation Manual,” Laminar Research, Columbia, SC, Nov. 2014, http://www.x-plane.com/files/manuals/X-Plane_Desktop_manual.pdf. Google Scholar