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Research Papers

Robust Attitude Control for Quadrotors Based on Parameter Optimization of a Nonlinear Disturbance Observer

[+] Author and Article Information
Shatadal Mishra

Robotics and Intelligent Systems Laboratory,
The Polytechnic School,
Arizona State University,
Mesa, AZ 85212
e-mail: smishr13@asu.edu

Todd Rakstad

Salt River Project,
P.O. Box 52025,
Phoenix, AZ 85072
e-mail: todd.rakstad@srpnet.com

Wenlong Zhang

Robotics and Intelligent Systems Laboratory,
The Polytechnic School,
Arizona State University,
Mesa, AZ 85212
e-mail: wenlong.zhang@asu.edu

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT,AND CONTROL. Manuscript received December 8, 2017; final manuscript received January 30, 2019; published online March 25, 2019. Assoc. Editor: Yongchun Fang.

J. Dyn. Sys., Meas., Control 141(8), 081003 (Mar 25, 2019) (12 pages) Paper No: DS-17-1608; doi: 10.1115/1.4042741 History: Received December 08, 2017; Revised January 30, 2019

This paper presents a nonlinear disturbance observer (NDOB) for active disturbance rejection in the attitude control loop for quadrotors. An optimization framework is developed for tuning the parameter in the NDOB structure, which includes the infinity-norm minimization of the weighted sum of noise-to-output transfer function and load disturbance sensitivity function. Subsequently, the minimization generates an optimal value of the parameter based on the tradeoff between disturbance rejection and noise propagation in the system. The proposed structure is implemented on PIXHAWK, a real-time embedded flight control unit. Simulation tests are carried out on a custom built, high-fidelity simulator providing physically accurate simulations. Furthermore, experimental flight tests are conducted to demonstrate the performance of the proposed approach. The system is injected with step, sinusoidal, and square wave disturbances, and the corresponding system tracking performance is recorded. Experimental results show that the proposed algorithm attenuates the disturbances better compared to just a baseline controller implementation. The proposed algorithm is computationally cheap, an active disturbance rejection technique and robust to exogenous disturbances.

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References

Leishman, J. G. , 2002, “ The Breguet-Richet Quad-Rotor Helicopter of 1907,” Vertiflite, 47(3), pp. 58–60. http://www.academia.edu/815361/The_breguet-richet_quad-rotor_helicopter_of_1907
Anderson, K. , and Gaston, K. J. , 2013, “ Lightweight Unmanned Aerial Vehicles Will Revolutionize Spatial Ecology,” Front. Ecol. Environ., 11(3), pp. 138–146. [CrossRef]
Lucieer, A. , Jong, S. M. D. , and Turner, D. , 2014, “ Mapping Landslide Displacements Using Structure From Motion (SfM) and Image Correlation of Multi-Temporal UAV Photography,” Prog. Phys. Geogr., 38(1), pp. 97–116. [CrossRef]
Bernardini, S. , Fox, M. , and Long, D. , 2014, “ Planning the Behaviour of Low-Cost Quadcopters for Surveillance Missions,” International Conference on Automated Planning and Scheduling (ICAPS), Portsmouth, NH, June 21–26, pp. 445–453. https://www.researchgate.net/publication/264898248_Planning_the_Behaviour_of_Low-Cost_Quadcopters_for_Surveillance_Missions
Goodrich, M. A. , Morse, B. S. , Gerhardt, D. , Cooper, J. L. , Quigley, M. , Adams, J. A. , and Humphrey, C. , 2008, “ Supporting Wilderness Search and Rescue Using a Camera-Equipped Mini UAV,” J. Field Rob., 25(1–2), pp. 89–110. [CrossRef]
Silvagni, M. , Tonoli, A. , Zenerino, E. , and Chiaberge, M. , 2017, “ Multipurpose UAV for Search and Rescue Operations in Mountain Avalanche Events,” Geomatics, Nat. Hazards Risk, 8(1), pp. 18–33. [CrossRef]
Koh, L. P. , and Wich, S. A. , 2012, “ Dawn of Drone Ecology: Low-Cost Autonomous Aerial Vehicles for Conservation,” Trop. Conserv. Sci., 5(2), pp. 121–132. [CrossRef]
van Gemert, J. C. , Verschoor, C. R. , Mettes, P. , Epema, K. , Koh, L. P. , and Wich, S. , 2014, “ Nature Conservation Drones for Automatic Localization and Counting of Animals,” European Conference on Computer Vision Workshops (ECCV), Zurich, Switzerland, Sept. 6–7, pp. 255–270. https://staff.science.uva.nl/p.s.m.mettes/papers/drones-eccvw14.pdf
Zhang, C. , and Kovacs, J. M. , 2012, “ The Application of Small Unmanned Aerial Systems for Precision Agriculture: A Review,” Precis. Agric., 13(6), pp. 693–712. [CrossRef]
Das, J. , Cross, G. , Qu, C. , Makineni, A. , Tokekar, P. , Mulgaonkar, Y. , and Kumar, V. , 2015, “ Devices, Systems, and Methods for Automated Monitoring Enabling Precision Agriculture,” IEEE International Conference on Automation Science and Engineering (CASE), Gothenburg, Sweden, Aug. 24–28, pp. 462–469.
Gómez-Candón, D. , De Castro, A. , and López-Granados, F. , 2014, “ Assessing the Accuracy of Mosaics From Unmanned Aerial Vehicle (UAV) Imagery for Precision Agriculture Purposes in Wheat,” Precis. Agric., 15(1), pp. 44–56. [CrossRef]
Mellinger, D. , Michael, N. , and Kumar, V. , 2012, “ Trajectory Generation and Control for Precise Aggressive Maneuvers With Quadrotors,” Int. J. Rob. Res., 31(5), pp. 664–674. [CrossRef]
Palunko, I. , Fierro, R. , and Cruz, P. , 2012, “ Trajectory Generation for Swing-Free Maneuvers of a Quadrotor With Suspended Payload: A Dynamic Programming Approach,” IEEE International Conference on Robotics and Automation (ICRA), St.Paul, MN, pp. 2691–2697.
Weiss, S. , Achtelik, M. , Kneip, L. , Scaramuzza, D. , and Siegwart, R. , 2011, “ Intuitive 3D Maps for Mav Terrain Exploration and Obstacle Avoidance,” J. Intell. Rob. Syst., 61(1), pp. 473–493. [CrossRef]
Cauchard, J. R. , Jane, L. E. , Zhai, K. Y. , and Landay, J. A. , 2015, “ Drone & Me: An Exploration Into Natural Human-Drone Interaction,” ACM International Joint Conference on Pervasive and Ubiquitous Computing, Osaka, Japan, Sept. 7–11, pp. 361–365. https://hci.stanford.edu/publications/2015/gestures/paper116.pdf
Zhang, R. , Quan, Q. , and Cai, K.-Y. , 2011, “ Attitude Control of a Quadrotor Aircraft Subject to a Class of Time-Varying Disturbances,” IET Control Theory Appl., 5(9), pp. 1140–1146. [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]
Alexis, K. , Nikolakopoulos, G. , and Tzes, A. , 2011, “ Switching Model Predictive Attitude Control for a Quadrotor Helicopter Subject to Atmospheric Disturbances,” Control Eng. Pract., 19(10), pp. 1195–1207. [CrossRef]
Bolandi, H. , Rezaei, M. , Mohsenipour, R. , Nemati, H. , and Smailzadeh, S. M. , 2013, “ Attitude Control of a Quadrotor With Optimized PID Controller,” Intell. Control Autom., 4(3), p. 335. [CrossRef]
Mokhtari, A. , Benallegue, A. , and Daachi, B. , 2005, “ Robust Feedback Linearization and GH/Sub/Spl Infin// Controller for a Quadrotor Unmanned Aerial Vehicle,” IEEE/RSJ International Conference on Intelligent Robots and Systems, Edmonton, AB, Canada, Aug. 2–6, pp. 1198–1203.
Derafa, L. , Benallegue, A. , and Fridman, L. , 2012, “ Super Twisting Control Algorithm for the Attitude Tracking of a Four Rotors UAV,” J. Franklin Inst., 349(2), pp. 685–699. [CrossRef]
Fresk, E. , and Nikolakopoulos, G. , 2013, “ Full Quaternion Based Attitude Control for a Quadrotor,” European Control Conference (ECC), Zurich, Switzerland, July 17–19, pp. 3864–3869.
Guerrero-Castellanos, J. , Marchand, N. , Hably, A. , Lesecq, S. , and Delamare, J. , 2011, “ Bounded Attitude Control of Rigid Bodies: Real-Time Experimentation to a Quadrotor Mini-Helicopter,” Control Eng. Pract., 19(8), pp. 790–797. [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]
Wang, H. , and Chen, M. , 2016, “ Trajectory Tracking Control for an Indoor Quadrotor UAV Based on the Disturbance Observer,” Trans. Inst. Meas. Control, 38(6), pp. 675–692. [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]
Bruhn, A. , Weickert, J. , and Schnörr, C. , 2005, “ Lucas/Kanade Meets Horn/Schunck: Combining Local and Global Optic Flow Methods,” Int. J. Comput. Vision, 61(3), pp. 211–231. [CrossRef]
Mishra, S. , and Zhang, W. , 2016, “ Hybrid Low Pass and De-Trending Filter for Robust Position Estimation of Quadcopters,” ASME Paper No. DSCC2016-9921. https://proceedings.asmedigitalcollection.asme.org/proceeding.aspx?articleid=2604524
Mellinger, D. , and Kumar, V. , 2011, “ Minimum Snap Trajectory Generation and Control for Quadrotors,” IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China, May 9–13, pp. 2520–2525.
García Carrillo, L. R. , Dzul López, A. E. , Lozano, R. , and Pégard, C. , 2013, Quad Rotorcraft Control, Springer, London.
Quan, Q. , 2017, Introduction to Multicopter Design and Control, Springer, Singapore.
Lee, T. , Leoky, M. , and McClamroch, N. H. , 2010, “ Geometric Tracking Control of a Quadrotor Uav on SE(3),” 49th IEEE Conference on Decision and Control (CDC), Atlanta, GA, Dec. 15–17, pp. 5420–5425.
Chen, W.-H. , Ballance, D. J. , Gawthrop, P. J. , and O'Reilly, J. , 2000, “ A Nonlinear Disturbance Observer for Robotic Manipulators,” IEEE Trans. Ind. Electron., 47(4), pp. 932–938. [CrossRef]
Chen, W.-H. , 2004, “ Disturbance Observer Based Control for Nonlinear Systems,” IEEE/ASME Trans. Mechatronics, 9(4), pp. 706–710. [CrossRef]
Jardin, M. R. , and Mueller, E. R. , 2009, “ Optimized Measurements of Unmanned-Air-Vehicle Mass Moment of Inertia With a Bifilar Pendulum,” J. Aircr., 46(3), p. 763. [CrossRef]

Figures

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Fig. 1

Frames of reference in a quadrotor

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Fig. 2

Work flow of a quadrotor

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Fig. 3

Nonlinear disturbance observer structure

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Fig. 4

System response in the presence of sinusoidal disturbance for different values of λ in simulations

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Fig. 5

System response in the presence of step disturbance for different values of λ in simulations

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Fig. 6

System response in the presence of square wave disturbance for different values of λ in simulations

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Fig. 7

A DJI F330 quadrotor used in experiments

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Fig. 8

Roll angle responses with sinusoidal disturbances for different values of λ in experiments

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Fig. 9

Roll angle responses with step disturbances for different values of λ in experiments

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Fig. 10

Roll angle responses with square wave disturbances for different values of λ in experiments

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Fig. 11

Roll angle responses to step disturbance while flying with baseline control and NDOB in experiments

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