0
Research Papers

Uncertainty and Disturbance Estimator-Based Robust Trajectory Tracking Control for a Quadrotor in a Global Positioning System-Denied Environment

[+] Author and Article Information
Qi Lu

Department of Mechanical Engineering,
Texas Tech University,
Lubbock, TX 79409
e-mail: qi.lu@ttu.edu

Beibei Ren

Department of Mechanical Engineering,
Texas Tech University,
Lubbock, TX 79409
e-mail: beibei.ren@ttu.edu

Siva Parameswaran

Department of Mechanical Engineering,
Texas Tech University,
Lubbock, TX 79409
e-mail: siva.parameswaran@ttu.edu

Qing-Chang Zhong

Department of Electrical and
Computer Engineering,
Illinois Institute of Technology,
Chicago, IL 60616
e-mail: zhongqc@ieee.org

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received March 21, 2016; final manuscript received August 16, 2017; published online November 8, 2017. Assoc. Editor: Manish Kumar.

J. Dyn. Sys., Meas., Control 140(3), 031001 (Nov 08, 2017) (15 pages) Paper No: DS-16-1155; doi: 10.1115/1.4037736 History: Received March 21, 2016; Revised August 16, 2017

This paper addresses the problem of autonomous trajectory tracking control for a quadrotor in a global positioning system (GPS)-denied environment using only onboard sensing. To achieve that goal, it requires accurate estimation of quadrotor states followed by proper control actions. For the position estimation in a GPS-denied environment, an open source high speed optical flow sensor PX4FLOW is adopted. As for the quadrotor control, there are several challenges due to its highly nonlinear system dynamics, such as underactuation, coupling, model uncertainties, and external disturbances. To deal with those challenges, the cascaded inner–outer uncertainty and disturbance estimator (UDE)-based robust control scheme has been developed and applied to the attitude and position control of a quadrotor. Extensive real flight experiments, including attitude stabilization, hover, disturbance rejection, trajectory tracking, and comparison with the proportional–integral–derivative (PID) controller are carried out to demonstrate the effectiveness of the developed UDE-based controllers.

Copyright © 2018 by ASME
Your Session has timed out. Please sign back in to continue.

References

Erginer, B. , and Altug, E. , 2007, “ Modeling and PD Control of a Quadrotor VTOL Vehicle,” IEEE Intelligent Vehicles Symposium (IVS), Istanbul, Turkey, June 13–15, pp. 894–899.
Bouabdallah, S. , 2007, “ Design and Control of Quadrotors With Application to Autonomous Flying,” Ph.D. thesis, École Polytechnique Federale de Lausanne, Lausanne, Switzerland.
Blösch, M. , Weiss, S. , Scaramuzza, D. , and Siegwart, R. , 2010, “ Vision Based MAV Navigation in Unknown and Unstructured Environments,” IEEE International Conference on Robotics and Automation (ICRA), Anchorage, AK, May 3–7, pp. 21–28.
Hoffmann, G. M. , Huang, H. , Waslander, S. L. , and Tomlin, C. J. , 2007, “ Quadrotor Helicopter Flight Dynamics and Control: Theory and Experiment,” AIAA Paper No. 2007-6461.
Hoffmann, G. M. , Huang, H. , Waslander, S. L. , and Tomlin, C. J. , 2011, “ Precision Flight Control for a Multi-Vehicle Quadrotor Helicopter Testbed,” Control Eng. Practice, 19(9), pp. 1023–1036. [CrossRef]
Mebarki, R. , Lippiello, V. , and Siciliano, B. , 2015, “ Nonlinear Visual Control of Unmanned Aerial Vehicles in GPS-Denied Environments,” IEEE Trans. Rob., 31(4), pp. 1004–1017. [CrossRef]
Honegger, D. , Meier, L. , Tanskanen, P. , and Pollefeys, M. , 2013, “ An Open Source and Open Hardware Embedded Metric Optical Flow CMOS Camera for Indoor and Outdoor Applications,” IEEE International Conference on Robotics and Automation (ICRA), Karlsruhe, Germany, May 6–10, pp. 1736–1741.
Choi, Y.-C. , and Ahn, H.-S. , 2015, “ Nonlinear Control of Quadrotor for Point Tracking: Actual Implementation and Experimental Tests,” IEEE/ASME Trans. Mechatronics, 20(3), pp. 1179–1192. [CrossRef]
Yun, B. , Peng, K. , and Chen, B. M. , 2007, “ Enhancement of GPS Signals for Automatic Control of a UAV Helicopter System,” IEEE International Conference on Control and Automation (ICCA), Guangzhou, China, May 30–June 1, pp. 1185–1189.
Michael, N. , Mellinger, D. , Lindsey, Q. , and Kumar, V. , 2010, “ The GRASP Multiple Micro-UAV Testbed,” IEEE Rob. Autom. Mag., 17(3), pp. 56–65. [CrossRef]
Goodarzi, F. A. , Lee, D. , and Lee, T. , 2015, “ Geometric Adaptive Tracking Control of a Quadrotor UAV on SE (3) for Agile Maneuvers,” ASME J. Dyn. Syst. Meas. Control, 137(9), p. 091007.
Islam, S. , Liu, P. , and El Saddik, A. , 2015, “ Robust Control of Four-Rotor Unmanned Aerial Vehicle With Disturbance Uncertainty,” IEEE Trans. Ind. Electron., 62(3), pp. 1563–1571. [CrossRef]
Ozaslan, T. , Shen, S. , Mulgaonkar, Y. , Michael, N. , and Kumar, V. , 2015, “ Inspection of Penstocks and Featureless Tunnel-Like Environments Using Micro UAVs,” Conference on Field and Service Robotics, Brisbane, Australia, Dec. 9–11, pp. 123–136.
Shen, S. , Michael, N. , and Kumar, V. , 2011, “ Autonomous Multi-Floor Indoor Navigation With a Computationally Constrained MAV,” IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China, May 9–13, pp. 20–25.
Dryanovski, I. , Morris, W. , and Xiao, J. , 2011, “ An Open-Source Pose Estimation System for Micro-Air Vehicles,” IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China, May 9–13, pp. 4449–4454.
Bachrach, A. , He, R. , and Roy, N. , 2009, “ Autonomous Flight in Unknown Indoor Environments,” Int. J. Micro Air Veh., 1(4), pp. 217–228. [CrossRef]
Ozaslan, T. , Loianno, G. , Keller, J. , Taylor, C. J. , Kumar, V. , Wozencraft, J. M. , and Hood, T. , 2017, “ Autonomous Navigation and Mapping for Inspection of Penstocks and Tunnels With MAVs,” IEEE Rob. Autom. Lett., 2(3), pp. 1740–1747. [CrossRef]
Zhang, X. , Xian, B. , Zhao, B. , and Zhang, Y. , 2015, “ Autonomous Flight Control of a Nano Quadrotor Helicopter in a GPS-Denied Environment Using On-Board Vision,” IEEE Trans. Ind. Electron., 62(10), pp. 6392–6403. [CrossRef]
Floreano, D. , Zufferey, J.-C. , Srinivasan, M. V. , and Ellington, C. , 2009, Flying Insects and Robots, Springer-Verlag, Berlin.
Xian, B. , Liu, Y. , Zhang, X. , Cao, M. , and Wang, F. , 2014, “ Hovering Control of a Nano Quadrotor Unmanned Aerial Vehicle Using Optical Flow,” Chinese Control Conference (CCC), Nanjing, China, July 28–30, pp. 8259–8264.
Kendoul, F. , Fantoni, I. , and Nonami, K. , 2009, “ Optic Flow-Based Vision System for Autonomous 3D Localization and Control of Small Aerial Vehicles,” Rob. Auton. Syst., 57(6), pp. 591–602. [CrossRef]
Garrido-Jurado, S. , Munoz-Salinas, R. , Madrid-Cuevas, F. J. , and Marin-Jimenez, M. J. , 2014, “ Automatic Generation and Detection of Highly Reliable Fiducial Markers Under Occlusion,” Pattern Recognit., 47(6), pp. 2280–2292. [CrossRef]
Ascending Technologies, 2015, “ AscTec Hummingbird,” Ascending Technologies GmbH, Krailling, Germany, accessed Sept. 1, 2017, http://www.asctec.de/en/
Bouabdallah, S. , Noth, A. , and Siegwart, R. , 2004, “ PID vs LQ Control Techniques Applied to an Indoor Micro Quadrotor,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Sendai, Japan, Sept. 28–Oct. 2, pp. 2451–2456.
Castillo, P. , Dzul, A. , and Lozano, R. , 2004, “ Real-Time Stabilization and Tracking of a Four-Rotor Mini Rotorcraft,” IEEE Trans. Control Syst. Technol., 12(4), pp. 510–516. [CrossRef]
Zhao, B. , Xian, B. , Zhang, Y. , and Zhang, X. , 2015, “ Nonlinear Robust Adaptive Tracking Control of a Quadrotor UAV Via Immersion and Invariance Methodology,” IEEE Trans. Ind. Electron., 62(5), pp. 2891–2902. [CrossRef]
Das, A. , Subbarao, K. , and Lewis, F. , 2009, “ Dynamic Inversion With Zero-Dynamics Stabilisation for Quadrotor Control,” IET Control Theory Appl., 3(3), pp. 303–314. [CrossRef]
Bouabdallah, S. , and Siegwart, R. , 2007, “ Full Control of a Quadrotor,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), San Diego, CA, Oct. 29–Nov. 2, pp. 153–158.
Ramirez-Rodriguez, H. , Parra-Vega, V. , Sanchez-Orta, A. , and Garcia-Salazar, O. , 2014, “ Robust Backstepping Control Based on Integral Sliding Modes for Tracking of Quadrotors,” J. Intell. Rob. Syst., 73(1), pp. 51–66. [CrossRef]
Dydek, Z. T. , Annaswamy, A. M. , and Lavretsky, E. , 2013, “ Adaptive Control of Quadrotor UAVs: A Design Trade Study With Flight Evaluations,” IEEE Trans. Control Syst. Technol., 21(4), pp. 1400–1406. [CrossRef]
Besnard, L. , Shtessel, Y. B. , and Landrum, B. , 2012, “ Quadrotor Vehicle Control Via Sliding Mode Controller Driven by Sliding Mode Disturbance Observer,” J. Franklin Inst., 349(2), pp. 658–684. [CrossRef]
Liu, H. , Wang, X. , and Zhong, Y. , 2015, “ Quaternion-Based Robust Attitude Control for Uncertain Robotic Quadrotors,” IEEE Trans. Ind. Inf., 11(2), pp. 406–415. [CrossRef]
Lee, K. , Back, J. , and Choy, I. , 2014, “ Nonlinear Disturbance Observer Based Robust Attitude Tracking Controller for Quadrotor UAVs,” Int. J. Control, Autom. Syst., 12(6), pp. 1266–1275. [CrossRef]
Dong, W. , Gu, G.-Y. , Zhu, X. , and Ding, H. , 2014, “ High-Performance Trajectory Tracking Control of a Quadrotor With Disturbance Observer,” Sens. Actuators A, 211, pp. 67–77. [CrossRef]
Zhong, Q.-C. , and Rees, D. , 2004, “ Control of Uncertain LTI Systems Based on an Uncertainty and Disturbance Estimator,” ASME J. Dyn. Syst. Meas. Control, 126(4), pp. 905–910. [CrossRef]
Stobart, R. , Kuperman, A. , and Zhong, Q.-C. , 2011, “ Uncertainty and Disturbance Estimator–Based Control for Uncertain LTI-SISO Systems With State Delays,” ASME J. Dyn. Syst. Meas. Control, 133(2), p. 024502. [CrossRef]
Talole, S. , Chandar, T. S. , and Kolhe, J. P. , 2011, “ Design and Experimental Validation of UDE Based Controller-Observer Structure for Robust Input-Ouput Linearisation,” Int. J. Control, 84(5), pp. 969–984. [CrossRef]
Talole, S. , and Phadke, S. , 2009, “ Robust Input-Output Linearisation Using Uncertainty and Disturbance Estimation,” Int. J. Control, 82(10), pp. 1794–1803. [CrossRef]
Ren, B. , Zhong, Q.-C. , and Chen, J. , 2015, “ Robust Control for a Class of Non-Affine Nonlinear Systems Based on the Uncertainty and Disturbance Estimator,” IEEE Trans. Ind. Electron., 62(9), pp. 5881–5888. [CrossRef]
Kuperman, A. , and Zhong, Q.-C. , 2011, “ Robust Control of Uncertain Nonlinear Systems With State Delays Based on an Uncertainty and Disturbance Estimator,” Int. J. Robust Nonlinear Control, 21(1), pp. 79–92. [CrossRef]
Kolhe, J. P. , Shaheed, M. , Chandar, T. S. , and Taloe, S. E. , 2013, “ Robust Control of Robot Manipulators Based on Uncertainty and Diturbance Estimation,” Int. J. Robust Nonlinear Control, 23(1), pp. 104–122. [CrossRef]
Lu, Q. , Ren, B. , Parameswaran, S. , and Zhong, Q.-C. , 2016, “ Robust Position Control of a Quadrotor Using Onboard Optical Flow Sensor,” ASME Paper No. DSCC2016-9812.
Dai, J. , Lu, Q. , Ren, B. , and Zhong, Q.-C. , 2015, “ Robust Attitude Tracking Control for a Quadrotor Based on the Uncertainty and Disturbance Estimator,” ASME Paper No. DSCC2015-9900.
Sanz, R. , Garcia, P. , Zhong, Q.-C. , and Albertos, P. , 2016, “ Robust Control of Quadrotors Based on an Uncertainty and Disturbance Estimator,” ASME J. Dyn. Syst. Meas. Control, 138(7), p. 071006. [CrossRef]
Ren, B. , Zhong, Q.-C. , and Dai, J. , 2017, “ Asymptotic Reference Tracking and Disturbance Rejection of UDE-Based Robust Control,” IEEE Trans. Ind. Electron., 64(4), pp. 3166–3176. [CrossRef]
Francis, B. A. , and Wonham, W. M. , 1976, “ The Internal Model Principle of Control Theory,” Automatica, 12(5), pp. 457–465. [CrossRef]

Figures

Grahic Jump Location
Fig. 1

Coordinate systems for a quadrotor model

Grahic Jump Location
Fig. 2

Quadrotor control scheme

Grahic Jump Location
Fig. 3

Experimental platform: top view (left) and bottom view (right)

Grahic Jump Location
Fig. 4

Experimental environment with patterned surface on the ground

Grahic Jump Location
Fig. 5

Attitude stabilization: (a) attitude and (b) control input

Grahic Jump Location
Fig. 6

Hover: (a) position, (b) velocity, and (c) attitude

Grahic Jump Location
Fig. 7

Control inputs for the hover test

Grahic Jump Location
Fig. 8

Horizontal coordinate plot for the hover test

Grahic Jump Location
Fig. 9

Disturbance rejection: position controller (a) position, (b) velocity, and (c) attitude

Grahic Jump Location
Fig. 10

Control inputs for the disturbance rejection: position controller test

Grahic Jump Location
Fig. 11

Disturbance rejection: attitude controller (a) position, (b) velocity, and (c) attitude

Grahic Jump Location
Fig. 12

Control inputs for the disturbance rejection: attitude controller test

Grahic Jump Location
Fig. 13

Trajectory tracking (a) position, (b) velocity, and (c) attitude

Grahic Jump Location
Fig. 14

Control inputs for the trajectory tracking test

Grahic Jump Location
Fig. 15

Horizontal coordinate plot for the trajectory tracking test

Grahic Jump Location
Fig. 16

Comparison with the PID controller for the hover test (a) nominal case and (b) sinusoidal disturbance rejection

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In