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Sensors and Control

  • Matko OrsagEmail author
  • Christopher Korpela
  • Paul Oh
  • Stjepan Bogdan
Chapter
Part of the Advances in Industrial Control book series (AIC)

Abstract

As with all UAS, sensors play an integral part in environmental interaction, pose estimation, and safety. Microelectronics and the software controlling them have drastically changed in recent years. The open-source software community continues to rapidly expand. The nature of the open-source software and maker communities has produced software and electronic components that can be easily combined creating new capabilities.

References

  1. 1.
    Åström KJ (1995) Wittenmark B (1995) Adaptive control. Addison-Wesley Series in Electrical Engineering. Addison-Wesley, BostonGoogle Scholar
  2. 2.
    Barczyk M, Lynch AF (2012) Integration of a triaxial magnetometer into a helicopter uav gps-aided ins. IEEE Trans Aerosp Electron Syst 48(4):2947–2960CrossRefGoogle Scholar
  3. 3.
    Boskovic JD, Mehra RK (2000) Multi-mode switching in flight control. In: Proceedings of the 19th digital avionics systems conference, 2000. DASC, vol 2 (2000), pp 6F2/1–6F2/8Google Scholar
  4. 4.
    Bouabdallah S, Siegwart R (2005) Backstepping and sliding-mode techniques applied to an indoor micro quadrotor. In: Proceedings of the IEEE international conference on robotics and automation (ICRA) (2005)Google Scholar
  5. 5.
    Bourquardez O, Mahony R, Guenard N, Chaumette F, Hamel T, Eck L (2009) Image-based visual servo control of the translation kinematics of a quadrotor aerial vehicle. IEEE Trans Robot 25(3):743–749CrossRefGoogle Scholar
  6. 6.
    Butler H (1992) Model reference adaptive control: from theory to practice. Prentice Hall International Series in Systems and Control Engineering, Prentice Hall, Upper Saddle RiverzbMATHGoogle Scholar
  7. 7.
    Chao HY, Cao YC, Chen YQ (2010) Autopilots for small unmanned aerial vehicles: a survey. Int J Control Autom Syst 8(1):36–44CrossRefGoogle Scholar
  8. 8.
    Cheah C-C, Wang D (1998) Learning impedance control for robotic manipulators. IEEE Trans Robot Autom 14(3):452–465CrossRefGoogle Scholar
  9. 9.
    Colaneri P (2009) Dwell time analysis of deterministic and stochastic switched systems. In: 2009 European control conference (ECC). IEEE, New York (2009), pp 15–31Google Scholar
  10. 10.
    Doyle JC, Francis BA, Tannenbaum AR (1991) Feedback control theory. Prentice Hall Professional Technical Reference, Prentice Hall, Upper Saddle RiverGoogle Scholar
  11. 11.
    Farrell J (1999) The global positioning system and inertial navigation. McGraw-Hill Education, New York (1999)Google Scholar
  12. 12.
    Fabresse FR, Caballero F, Maza I, Ollero A (2014) Localization and mapping for aerial manipulation based on range-only measurements and visual markers. In: Proceedings of 2014 IEEE international conference on robotics and automation (ICRA) (2014), pp 2100–2106Google Scholar
  13. 13.
    Grewal MS, Andrews AP (1993) Kalman filtering: theory and practiceGoogle Scholar
  14. 14.
    Haus T, Orsag M, Bogdan S (2014) Visual target localization with the spincopter. J Intell Robot Syst 74(1–2):45–57CrossRefGoogle Scholar
  15. 15.
    Hespanha JP (2004) Uniform stability of switched linear systems: extensions of lasalle’s invariance principle. IEEE Trans Autom Control 49(4):470–482CrossRefzbMATHMathSciNetGoogle Scholar
  16. 16.
    Hespanha JP, Morse AS (1999) Stability of switched systems with average dwell-time. Proceedings of the 38th IEEE conference on decision and control (Cat. No.99CH36304), vol 3 (1999)Google Scholar
  17. 17.
    Hogan N (1984) Impedance control: an approach to manipulation. In Proceedings of the American Control Conference 1984:304–313Google Scholar
  18. 18.
    Hsia TCS (1989) A new technique for robust control of servo systems. Industrial Electronics, IEEE Transactions on 36(1):1–7CrossRefGoogle Scholar
  19. 19.
    Jimenez-Cano AE, Martin J, Heredia G, Ollero A, Cano R (2013) Control of an aerial robot with multi-link arm for assembly tasks. In: 2013 IEEE international conference on robotics and automation (ICRA), May 2013, pp 4916–4921Google Scholar
  20. 20.
    Kim J-S, Kim J-H, Park J-M, Park S-M, Choe W-Y, Heo H (2008) Auto tuning pid controller based on improved genetic algorithm for reverse osmosis plant. World Acad Sci Eng Technol 47:384–389Google Scholar
  21. 21.
    Korpela C, Orsag M, Pekala M, Oh P (2013) Dynamic stability of a mobile manipulating unmanned aerial vehicle. In: 2013 IEEE international conference on robotics and automation (ICRA), May 2013, pp 4922–4927Google Scholar
  22. 22.
    Korpela C, Orsag M, Paul O (2014) Hardware-in-the-loop verification for mobile manipulating unmanned aerial vehicles. J Intell Robot Syst 73(1–4):725–736CrossRefGoogle Scholar
  23. 23.
    Kovacic Z, Bogdan S, Puncec M (2003) Adaptive control based on sensitivity model-based adaptation of lead-lag compensator parameters. In: 2003 IEEE international conference on industrial technology, vol 1, December 2003, pp 321–326Google Scholar
  24. 24.
    Landau ID (2011) Adaptive control. Communications and control engineering, Springer, LondonCrossRefGoogle Scholar
  25. 25.
    Larsen TD, Andersen NA, Ravn O, Poulsen NK (1998) Incorporation of time delayed measurements in a discrete-time kalman filter. In: Proceedings of the 37th IEEE conference on decision and control, 1998, vol 4, December 1998, pp 3972–3977Google Scholar
  26. 26.
    Leick A (2004) GPS satellite surveying. Wiley, New JerseyGoogle Scholar
  27. 27.
    Leishman RC, Macdonald JC, Beard RW, McLain TW (2014) Quadrotors and accelerometers: state estimation with an improved dynamic model. IEEE Control Syst 34(1):28–41CrossRefMathSciNetGoogle Scholar
  28. 28.
    Levine WS (1996) The control handbook. CRC Press, Boca RatonzbMATHGoogle Scholar
  29. 29.
    Lim H, Park J, Lee D, Kim HJ (2012) Build your own quadrotor: opensource projects on unmanned aerial vehicles. IEEE Robot Autom Mag 19(3):33–45CrossRefGoogle Scholar
  30. 30.
    Macdonald J, Leishman R, Beard R, McLain T (2014) Analysis of an improved imu-based observer for multirotor helicopters. J Intell Robot Syst 74(3–4):1049–1061CrossRefGoogle Scholar
  31. 31.
    Madani T, Benallegue A (2006) Backstepping control for a quadrotor helicopter. In: 2006 IEEE/RSJ international conference on intelligent robots and systems, October 2006, pp 3255–3260Google Scholar
  32. 32.
    Masubuchi I, Kato J, Saeki M, Ohara A (2004) Gain-scheduled controller design based on descriptor representation of lpv systems: application to flight vehicle control. In: 43rd IEEE conference on decision and control, 2004. CDC, vol 1, pp 815–820Google Scholar
  33. 33.
    Mellinger D, Lindsey Q, Shomin M, Kumar V (2011) Design, modeling, estimation and control for aerial grasping and manipulation. In: Proceedings of the IEEE/RSJ international intelligent robots and systems (IROS) Conference, pp 2668–2673Google Scholar
  34. 34.
    Miskovic N, Vukic Z, Bibuli M, Caccia M, Bruzzone G (2009) Marine vehicles’ line following controller tuning through selfoscillation experiments. Proceedings of the 2009 17th mediterranean conference on control and automation, MED ’09. IEEE Computer Society, Washington, DC, USA, pp 916–921CrossRefGoogle Scholar
  35. 35.
    Misra P, Enge P (2006) Global positioning system: signals, measurements and performance, 2nd ednGoogle Scholar
  36. 36.
    Nichols RA, Reichert RT, Rugh WJ (1993) Gain scheduling for h-infinity controllers: a flight control example. IEEE Trans Control Syst Technol 1(2):69–79CrossRefGoogle Scholar
  37. 37.
    Orsag M, Haus T, Palunko I, Bogdan S (2015) State estimation, robust control and obstacle avoidance for multicopter in cluttered environments: Euroc experience and results. 2015 international conference on unmanned aircraft systems (ICUAS). IEEE, New Jersey, pp 455–461CrossRefGoogle Scholar
  38. 38.
    Orsag M, Korpela C, Bogdan S, Paul O (2014) Hybrid adaptive control for aerial manipulation. J Intell Robot Syst 73(1–4):693–707CrossRefGoogle Scholar
  39. 39.
    Pounds PEI, Bersak DR, Dollar AM (2011) Grasping from the air: hovering capture and load stability. In: Proceedings IEEE international robotics and automation (ICRA) Conference, pp 2491–2498Google Scholar
  40. 40.
    Romero H, Benosman R, Lozano R (2006) Stabilization and location of a four rotor helicopter applying vision. In: American control conferenceGoogle Scholar
  41. 41.
    Rozenwasser E, Yusupov R (1999) Sensitivity of automatic control systems. CRC Press, Boca Raton, Control SeriesCrossRefGoogle Scholar
  42. 42.
    Seborg DE, Mellichamp DA, Edgar TF, Doyle FJ III (2010) Process dynamics and control. Wiley, New JerseyGoogle Scholar
  43. 43.
    Slotine JJE, Li W (1991) Applied nonlinear control, Englewood Cliffs, NJ, Prentice hallGoogle Scholar
  44. 44.
    Vukic Z (2003) Nonlinear Control Systems, Taylor and Francis, CRC PressGoogle Scholar
  45. 45.
    Welch G, Bishop G (2006) An introduction to the kalman filter. Department of computer science, university of north carolinaGoogle Scholar
  46. 46.
    Zachariah D (2013) Estimation for Sensor Fusion and Sparse Signal Processing. PhD thesis, KTH Royal Institute of TechnologyGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Matko Orsag
    • 1
    Email author
  • Christopher Korpela
    • 2
  • Paul Oh
    • 3
  • Stjepan Bogdan
    • 1
  1. 1.Laboratory for Robotics and Intelligent Control Systems, Faculty of Electrical Engineering and ComputingUniversity of ZagrebZagrebCroatia
  2. 2.Department of Electrical Engineering and Computer ScienceUnited States Military AcademyWest PointUSA
  3. 3.Department of Mechanical EngineeringUniversity of Nevada Las VegasLas VegasUSA

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