Model predictive quadrotor control: attitude, altitude and position experimental studies
Model predictive quadrotor control: attitude, altitude and position experimental studies
- Author(s): K. Alexis ; G. Nikolakopoulos ; A. Tzes
- DOI: 10.1049/iet-cta.2011.0348
For access to this article, please select a purchase option:
Buy article PDF
Buy Knowledge Pack
IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.
Thank you
Your recommendation has been sent to your librarian.
- Author(s): K. Alexis 1 ; G. Nikolakopoulos 2 ; A. Tzes 1
-
-
View affiliations
-
Affiliations:
1: Department of Electrical and Computer Engineering, University of Patras, Greece
2: Computer Science, Electrical and Space Engineering Department, Lulea University of Technology, Sweden
-
Affiliations:
1: Department of Electrical and Computer Engineering, University of Patras, Greece
- Source:
Volume 6, Issue 12,
16 August 2012,
p.
1812 – 1827
DOI: 10.1049/iet-cta.2011.0348 , Print ISSN 1751-8644, Online ISSN 1751-8652
This study addresses the control problem of an unmanned quadrotor in an indoor environment where there is lack of absolute localisation data. Based on an attached inertia measurement unit, a sonar and an optic-flow sensor, the state vector is estimated using sensor fusion algorithms. A novel switching model predictive controller is designed in order to achieve precise trajectory control, under the presence of forcible wind gusts. The quadrotor’s attitude, altitude and horizontal linearised dynamics result in a set of piecewise affine models, enabling the controller to account for a larger part of the quadrotor’s flight envelope while modelling the effects of atmospheric disturbances as additive-affine terms in the system. The proposed controller algorithm accounts for the state and actuation constraints of the system. The controller is implemented on a quadrotor prototype in indoor position tracking, hovering and attitude manoeuvres experiments. The experimental results indicate the overall system’s efficiency in position/altitude/attitude set-point manoeuvres.
Inspec keywords: attitude control; sensor fusion; trajectory control; autonomous aerial vehicles; helicopters; time-varying systems; sonar; predictive control; optical sensors; aircraft control; control system synthesis; flow sensors
Other keywords:
Subjects: Spatial variables control; Sensor fusion; Control system analysis and synthesis methods; Aerospace control; Mobile robots; Optimal control; Time-varying control systems
References
-
-
1)
- Alexis, K., Nikolakopoulos, G., Tzes, A., Dritsas, L.: `Coordination of helicopter UAVs for aerial forest-fire surveillance', Applications of intelligent control to engineering systems, 2009, Springer, The Netherlands, p. 169–193.
-
2)
- R.E. Moore . (1979) Methods and applications of interval analysis.
-
3)
- Yang, X., Pota, H., Garrat, M.: `Design of a gust-attenuation controller for landing operations of unmanned autonomous helicopters', 18thIEEE Int. Conf. on Control Applications, July 2009, Saint Petersburg, Russia, p. 1300–1305.
-
4)
- Gray, S.: `Cooperation between UAVs in search and destroy mission', American Institute of Aeronautics and Astronautics (AIAA) Guidance, Navigation, and Control Conf. and Exhibit, 2003, Austin, USA.
-
5)
- G. Raffo , M. Ortega , F. Rubio . An integral predictive/nonlinear control structure for a quadrotor helicopter. Automatica , 29 - 39
-
6)
- Ryan, A., Hedrick, J.: `A mode-switching path planner for UAV-assisted search and rescue', 44thIEEE Conf. Decision and Control, 2005 European Control Conf., CDC-ECC ’05, 2005, Seville, Spain, p. 1471–1476.
-
7)
- Abraham, R.H., Bachrach, Roy N.: `Autonomous flight in unstructured and unkown indoor environments', European Micro Aerial Vehicle Conf. and Flight Competition (EMAV), ASTI, D-CIS LAB, TUDelft, 14–17 September 2009, THALES, The Netherlands.
-
8)
- Murphy, D., Cycon, J.: `Applications for mini VTOL UAV for law enforcement', Information and Training Technologies for Law Enforcement, November 1998, Boston, MA, USA.
-
9)
- Xsens: ‘Xsens MTi-G’, http://www.xsens.com/en/general/mti-g, 2008.
-
10)
- Huang, H.: `Aerodynamics and control of autonomous quadrotor helicopters in aggressive maneuvering', Int. Conf. on Robotics and Automation, 2009, Kobe, Japan.
-
11)
- Bouabdallah, S.: `Design and control of quadrotors with application to autonomous flying', 2007, PhD, EPFL, Lausanee, STI School of Engineering.
-
12)
- A.H. González , D. Odloak . Robust model predictive control with zone control. IET Control Theory Appl. , 1 , 121 - 135
-
13)
- G. Caprari , A. Breitenmoser , W. Fischer . Highly compact robots for inspection of power plants. J. Field Robot. , 1 , 47 - 68
-
14)
- Benallegue, A., Mokhtari, A., Fridman, L.: `Feedback linearization and high order sliding mode observer for a quadrotor UAV', Int. Workshop Variable Structure Systems (VSS’06), 2006, Alghero, Sardinia, p. 365–372.
-
15)
- Bouabdallah, S., Siegwart, R.: `Full control of a quadrotor', IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, 2007, IROS 2007, 2007, San Diego, CA, USA, p. 153–158.
-
16)
- Alexis, K., Nikolakopoulos, G., Tzes, A.: `Design and experimental verification of a constrained finite time optimal control scheme for the attitude control of a quadrotor helicopter subject to wind gusts', 2010 Int. Conf. on Robotics and Automation, 2010, Anchorage, AK, USA, p. 1636–1641.
-
17)
- Girard, A., Howell, A., Hedrick, J.: `Border patrol and surveillance missions using multiple unmanned air vehicles', 43rdIEEE Conf. on Decision and Control, December 2004, Atlantis, Paradise Island, Bahamas, p. 620–625, vol. 1.
-
18)
- E.F. Camacho , C. Bordons . (2004) Model predictive control.
-
19)
- M.V. Srinivasan . An image-interpolation technique for the computation of optic flow and egomotion. Biol. Cybern. , 5 , 401 - 415
-
20)
- Z. Zuo . Trajectory tracking control design with command-filtered compensation for a quadrotor. IET Control Theory Appl. , 11 , 2343 - 2355
-
21)
- A. Das , K. Subbarao , F. Lewis . Dynamic inversion with zero-dynamics stabilisation for quadrotor control. IET Control Theory Appl. , 3 , 303 - 314
-
22)
- Costelo, M.F.: `A theory of the analysis of rotorcraft operation in atmospheric turbulence', 1992, PhD, Georgia Institute of Technology, School of Aerospace Engineering.
-
23)
- Centeye: ‘Tam2 and Tam4 vision chips’, http://centeye.com/technology/vision-chips, WA, USA, February 2011.
-
24)
- D. Simon . (2006) Optimal state estimation: Kalman, H infinity and nonlinear approaches.
-
25)
- N. Poursafar , H. Taghirad , M. Haeri . Model predictive control of non-linear discrete time systems: a linear matrix inequality approach. IET Control Theory Appl. , 10 , 1922 - 1932
-
26)
- Lange, S., Sunderhauf, N., Protzel, P.: `A vision based onboard approach for landing and position control of an autonomous multirotor UAV in GPS-denied environments', International Conference on Advanced Robotics (ICAR), Munich Marriot Hotel, June 2009, Germany, p. 22–24..
-
27)
- (2005) Autonomous vehicles in support of naval operations.
-
28)
- Sarris, Z.: `Survey of UAV applications in civil markets', Mediterranean Conf. on Control and Automation, 2001, Ancona, Italy.
-
29)
- K. Alexis , G. Nikolakopoulos , A. Tzes . Switching model predictive attitude control for a quadrotor helicopter subject to atmospheric disturbances. Control Eng. Pract. , 10 , 1195 - 1207
-
30)
- Hoffmann, G.M., Huang, H., Waslander, S.L., Tomlin, C.J.: `Quadrotor helicopter flight dynamics and control: theory and experiment', Proc. Guidance, Navigation, and Control Conf., 2007.
-
31)
- J.-C. Zufferey . (2008) Bio-inspired flying robots – experimental synthesis of autonomous indoor flyers.
-
32)
- Bouabdallah, S., Noth, A., Siegwart, R.: `PID vs LQ control techniques applied to an indoor micro quadrotor', Proc. 2004 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, 2004, (IROS 2004), 2004, p. 2451–2456, vol. 3.
-
33)
- Alexis, K., Nikolakopoulos, G., Tzes, A.: `Constrained optimal attitude control of a quadrotor helicopter subject to wind-gusts: experimental studies', American Control Conf. '10, 2010, Baltimore, USA, p. 4451–4455.
-
34)
- S.R. Herwitz , L.F. Johnson , S.E. Dunagan . Imaging from an unmanned aerial vehicle: agricultural surveillance and decision support. Comput. Electron. Agric. , 1 , 49 - 61
-
35)
- A. Das , F. Lewis , K. Subbarao . Backstepping approach for controlling a quadrotor using lagrange form dynamics. J. Intell. Robot. Syst. , 127 - 151
-
36)
- K. Alexis , G. Nikolakopoulos , A. Tzes . Autonomous quadrotor position and attitude PID/PIDD control in GPS-denied environments. Int. Rev. Autom. Control , 421 - 430
-
37)
- R. Zhang , Q. Quan , K.-Y. Cai . Attitude control of a quadrotor aircraft subject to a class of time-varying disturbances. IET Control Theory Appl. , 9 , 1140 - 1146
-
38)
- B. Mettler . (2003) Identification modeling and characteristics of miniature rotorcraft.
-
1)
Related content
