Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
169 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

High-Gain Disturbance Observer for Robust Trajectory Tracking of Quadrotors (2305.19115v2)

Published 30 May 2023 in cs.RO

Abstract: This paper presents a simple method to boost the robustness of quadrotors in trajectory tracking. The presented method features a high-gain disturbance observer (HGDO) that provides disturbance estimates in real-time. The estimates are then used in a trajectory control law to compensate for disturbance effects. We present theoretical convergence results showing that the proposed HGDO can quickly converge to an adjustable neighborhood of actual disturbance values. We will then integrate the disturbance estimates with a typical robust trajectory controller, namely sliding mode control (SMC), and present Lyapunov stability analysis to establish the boundedness of trajectory tracking errors. However, our stability analysis can be easily extended to other Lyapunov-based controllers to develop different HGDO-based controllers with formal stability guarantees. We evaluate the proposed HGDO-based control method using both simulation and laboratory experiments in various scenarios and in the presence of external disturbances. Our results indicate that the addition of HGDO to a quadrotor trajectory controller can significantly improve the accuracy and precision of trajectory tracking in the presence of external disturbances.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (45)
  1. Modeling of the urban gust environment with application to autonomous flight. In AIAA Atmospheric Flight Mechanics Conference and Exhibit, page 6565, 2008. doi: 10.2514/6.2008-6565.
  2. Quasi-steady in-ground-effect model for single and multirotor aerial vehicles. AIAA Journal, 58(12):5318–5331, 2020. doi: 10.2514/1.J059223.
  3. Numerical studies on modeling the near-and far-field wake vortex of a quadrotor in forward flight. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 236(6):1166–1183, 2022. doi: 10.1177/09544100211029074.
  4. Flatness-based model predictive control for quadrotor trajectory tracking. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 6740–6745. IEEE, 2018. doi: 10.1109/IROS.2018.8594012.
  5. Finite-time sliding mode control for singularly perturbed pde systems. Journal of the Franklin Institute, 360(2):841–861, 2023. doi: 10.1016/j.jfranklin.2022.11.037.
  6. Nonlinear mpc for quadrotor fault-tolerant control. IEEE Robotics and Automation Letters, 7(2):5047–5054, 2022. doi: 10.1109/LRA.2022.3154033.
  7. Sliding mode control of a quadrotor helicopter. In Proceedings of the 45th IEEE Conference on Decision and Control, pages 4957–4962. IEEE, 2006. doi: 10.1109/CDC.2006.377588.
  8. Nonsingular terminal sliding mode control for a quadrotor uav with a total rotor failure. Aerospace Science and Technology, 98:105716, 2020. doi: 10.1016/j.ast.2020.105716.
  9. Second order sliding mode control for a quadrotor uav. ISA transactions, 53(4):1350–1356, 2014. doi: 10.1016/j.isatra.2014.03.010.
  10. 3d trajectory tracking control for a thrust-propelled vehicle with time-varying disturbances. International Journal of Control, Automation and Systems, 17:1978–1986, 2019. doi: 10.1007/s12555-018-0331-3.
  11. Position trajectory tracking of a quadrotor helicopter based on l1 adaptive control. In 2013 European Control Conference (ECC), pages 3346–3353. IEEE, 2013. doi: 10.1515/auto-2013-1035.
  12. 1 bit encoding–decoding-based event-triggered fixed-time adaptive control for unmanned surface vehicle with guaranteed tracking performance. Control Engineering Practice, 135:105513, 2023. doi: 10.1016/j.conengprac.2023.105513.
  13. Enforcing robust control guarantees within neural network policies. arXiv preprint arXiv:2011.08105, 2020. doi: 10.48550/arXiv.2011.08105.
  14. Cascade flight control of quadrotors based on deep reinforcement learning. IEEE Robotics and Automation Letters, 7(4):11134–11141, 2022. doi: 10.1109/LRA.2022.3196455.
  15. A time delay controller for systems with unknown dynamics. In 1988 American Control Conference, pages 904–913, 1988. doi: 10.23919/ACC.1988.4789852.
  16. Altitude control of a quad-rotor system by using a time-delayed control method. Journal of Institute of Control, Robotics and Systems, 20(7):724–729, 2014. doi: 10.5302/J.ICROS.2014.13.1947.
  17. Sliding mode disturbance observer-based control for a reusable launch vehicle. Journal of guidance, control, and dynamics, 29(6):1315–1328, 2006. doi: 10.2514/1.20151.
  18. Generalized extended state observer based high precision attitude control of quadrotor vehicles subject to wind disturbance. IEEE Access, 6:32349–32359, 2018. doi: 10.1109/ACCESS.2018.2842198.
  19. SE Talole and SB Phadke. Model following sliding mode control based on uncertainty and disturbance estimator. ASME Journal of Dynamic Systems, Measurement, and Control, 130(3):034501, 2008.
  20. TS Chandar and SE Talole. Improving the performance of ude-based controller using a new filter design. Nonlinear Dynamics, 77(3):753–768, 2014. doi: 10.1007/s11071-014-1337-x.
  21. Control of uncertain lti systems based on an uncertainty and disturbance estimator. J. Dyn. Sys., Meas., Control, 126(4):905–910, 2004. doi: 10.1115/1.1850529.
  22. Robust point-to-point iterative learning control for constrained systems: A minimum energy approach. International Journal of Robust and Nonlinear Control, 32(18):10139–10161, 2022. doi: 10.1002/rnc.6354.
  23. Geometric adaptive robust hierarchical control for quadrotors with aerodynamic damping and complete inertia compensation. IEEE Transactions on Industrial Electronics, 69(12):13213–13224, 2021. doi: 10.1109/TIE.2021.3137615.
  24. Robust observer-based dynamic sliding mode controller for a quadrotor uav. IEEE access, 6:45846–45859, 2018. doi: 10.1109/ACCESS.2018.2866208.
  25. Finite-time control for a uav system based on finite-time disturbance observer. Aerospace Science and Technology, 129:107825, 2022. doi: 10.1016/j.ast.2022.107825.
  26. Vladimir Stojanović. Fault-tolerant control of a hydraulic servo actuator via adaptive dynamic programming. Mathematical Modelling and Control, 2023. doi: 10.3934/mmc.2023016.
  27. Hassan K Khalil. High-gain observers in nonlinear feedback control. SIAM, 2017a. doi: 10.1002/rnc.3051.
  28. An adaptive high-gain observer for nonlinear systems. Automatica, 46(9):1483–1488, 2010. doi: 10.1016/j.automatica.2010.06.004.
  29. High-gain disturbance observer-based backstepping control with output tracking error constraint for electro-hydraulic systems. IEEE Transactions on Control Systems Technology, 23(2):787–795, 2015. doi: 10.1109/TCST.2014.2325895.
  30. Hassan K Khalil. Extended high-gain observers as disturbance estimators. SICE Journal of Control, Measurement, and System Integration, 10(3):125–134, 2017b. doi: 10.9746/jcmsi.10.125.
  31. Uncertainty and disturbance estimation for quadrotor control using extended high-gain observers: Experimental implementation. In Dynamic Systems and Control Conference, volume 58288, page V002T01A003. American Society of Mechanical Engineers, 2017. doi: 10.1115/DSCC2017-5204.
  32. Composite disturbance rejection attitude control for quadrotor with unknown disturbance. IEEE Transactions on Industrial Electronics, 67(8):6894–6903, 2019. doi: 10.1109/TIE.2019.2937065.
  33. Uncertainty and disturbance estimator-based global trajectory tracking control for a quadrotor. IEEE/ASME Transactions on Mechatronics, 25(3):1519–1530, 2020. doi: 10.1109/TMECH.2020.2978529.
  34. A high-gain observer approach to robust trajectory estimation and tracking for a multi-rotor uav. arXiv preprint arXiv:2103.13429, 2021. doi: 10.48550/arXiv.2103.13429.
  35. Output feedback control design using extended high-gain observers and dynamic inversion with projection for a small scaled helicopter. Automatica, 133:109883, 2021. doi: 10.1016/j.automatica.2021.109883.
  36. Robust control of small-scale unmanned helicopter with matched and mismatched disturbances. Journal of the Franklin Institute, 353(18):4803–4820, 2016. doi: 10.1016/j.jfranklin.2016.09.016.
  37. Multirotor aerial vehicles: Modeling, estimation, and control of quadrotor. IEEE robotics & automation magazine, 19(3):20–32, 2012. doi: 10.1109/MRA.2012.2206474.
  38. Full control of a quadrotor. In 2007 IEEE/RSJ international conference on intelligent robots and systems, pages 153–158. Ieee, 2007. doi: 10.1109/IROS.2007.4399042.
  39. Towards intelligent miniature flying robots. In Field and Service Robotics: Results of the 5th International Conference, pages 429–440. Springer, 2006. doi: 10.1007/978-3-540-33453-8_36.
  40. Euler-lagrange modeling and control of quadrotor uav with aerodynamic compensation. In 2022 International Conference on Unmanned Aircraft Systems (ICUAS), pages 369–377. IEEE, 2022. doi: 10.1109/ICUAS54217.2022.9836215.
  41. Richard L Wheeden. Measure and integral: an introduction to real analysis, volume 308. CRC press, 2015.
  42. Crazyflie 2.1, 2023. URL https://store.bitcraze.io/products/crazyflie-2-1.
  43. TR Beal. Digital simulation of atmospheric turbulence for dryden and von karman models. Journal of Guidance, Control, and Dynamics, 16(1):132–138, 1993. doi: 10.2514/3.11437.
  44. High-gain observers in the presence of measurement noise: A switched-gain approach. Automatica, 45(4):936–943, 2009. doi: 10.1016/j.automatica.2008.11.012.
  45. On the performance of high-gain observers with gain adaptation under measurement noise. Automatica, 47(10):2165–2176, 2011. doi: 10.1016/j.automatica.2011.08.002.
Citations (11)

Summary

We haven't generated a summary for this paper yet.