Skip to main content
SpringerLink
Log in

Robust Observer-based Trajectory Tracking Control for Unmanned Aerial Manipulator

  • Regular Papers
  • Robot and Applications
  • Published:
International Journal of Control, Automation and Systems Aims and scope Submit manuscript

Abstract

This paper aims at the stable motion control problem of the unmanned aerial manipulator (UAM) with internal interactions and external environment disturbances, and proposes a robust observer-based trajectory tracking control framework. Considering the large-dimensional nonlinearity and underactuated of UAM dynamics, a separate control scheme is adopted, including a geometric controller for quadrotor UAV and prescribed performance control (PPC) method for onboard active manipulator (OAM). In particular, an observer-based geometric control scheme is developed for the position and attitude tracking of the quadrotor UAV to ensure steady flight, where the quadrotor UAV attitude is represented by the rotation matrix to eliminate singularities or ambiguities. Subsequently, an observer-based PPC method is presented for the OAM to guarantee the prescribed transient and steady-state performance responses. Finally, the simulation comparisons and experimental study validate the effectiveness and performances of the proposed control framework.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. P. Chermprayong, K. Zhang, F. Xiao, and M. Kovac, “An integrated delta manipulator for aerial repair: A new aerial robotic system,” IEEE Robotics & Automation Magazine, vol. 26, no. 1, pp. 54–66, March 2019.

    Article  Google Scholar 

  2. M. Orsag, C. Korpela, S. Bogdan, and P. Oh, “Dexterous aerial robots—mobile manipulation using unmanned aerial systems,” IEEE Transactions on Robotics, vol. 33, no. 6, pp. 1453–1466, December 2017.

    Article  Google Scholar 

  3. H. Zhong, Z. Miao, Y. Wang, J. Mao, L. Li, H. Zhang, Y. Chen, and R. Fierro, “A practical visual servo control for aerial manipulation using a spherical projection model,” IEEE Transactions on Industrial Electronics, vol. 67, no. 12, pp. 10564–10574, December 2020.

    Article  Google Scholar 

  4. J. R. Kutia, K. A. Stol, and W. Xu, “Aerial manipulator interactions with trees for canopy sampling,” IEEE/ASME Transactions on Mechatronics, vol. 23, no. 4, pp. 1740–1749, August 2018.

    Article  Google Scholar 

  5. T. Ikeda, S. Yasui, M. Fujihara, K. Ohara, S. Ashizawa, A. Ichikawa, A. Okino, T. Oomichi, and T. Fukuda, “Wall contact by octo-rotor UAV with one DoF manipulator for bridge inspection,” Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5122–5127, 2017.

  6. S. Hamaza, I. Georgilas, M. Fernandez, P. Sanchez, T. Richardson, G. Heredia, and A. Ollero, “Sensor installation and retrieval operations using an unmanned aerial manipulator,” IEEE Robotics and Automation Letters, vol. 4, no. 3, pp. 2793–2800, July 2019.

    Article  Google Scholar 

  7. S. Kim, S. Choi, and H. J. Kim, “Aerial manipulation using a quadrotor with a two DOF robotic arm,” Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4990–4995, 2013.

  8. S. Kim, H. Seo, S. Choi, and H. J. Kim, “Vision-guided aerial manipulation using a multirotor with a robotic arm,” IEEE/ASME Transactions on Mechatronics, vol. 21, no. 4, pp. 1912–1923, August 2016.

    Article  Google Scholar 

  9. A. Santamaria-Navarro, P. Grosch, V. Lippiello, J. Solà, and J. Andrade-Cetto, “Uncalibrated visual servo for unmanned aerial manipulation,” IEEE/ASME Transactions on Mechatronics, vol. 22, no. 4, pp. 1610–1621, August 2017.

    Article  Google Scholar 

  10. H. Lee and H. J. Kim, “Estimation, control, and planning for autonomous aerial transportation,” IEEE Transactions on Industrial Electronics, vol. 64, no. 4, pp. 3369–3379, April 2017.

    Article  Google Scholar 

  11. S. Islam, P. X. Liu, A. E. Saddik, R. Ashour, J. Dias, and L. D. Seneviratne, “Artificial and virtual impedance interaction force reflection-based bilateral shared control for miniature unmanned aerial vehicle,” IEEE Transactions on Industrial Electronics, vol. 66, no. 1, pp. 329–337, January 2019.

    Article  Google Scholar 

  12. F. Ruggiero, M. A. Trujillo, R. Cano, H. Ascorbe, A. Viguria, C. Peréz, V. Lippiello, A. Ollero, and B. Siciliano, “A multilayer control for multirotor UAVs equipped with a servo robot arm,” Proc. of the IEEE International Conference on Robotics and Automation, pp. 4014–4020, 2015.

  13. A. E. Jimenez-Cano, J. Martin, G. Heredia, A. Ollero, and R. Cano, “Control of an aerial robot with multi-link arm for assembly tasks,” Proc. of the IEEE International Conference on Robotics and Automation, pp. 4916–4921, 2013.

  14. F. Dong, K. You, and J. Zhang, “Flight control for UAV loitering over a ground target with unknown maneuver,” IEEE Transactions on Control Systems Technology, vol. 28, no. 6, pp. 2461–2473, November 2019.

    Article  Google Scholar 

  15. W. Cai, J. She, M. Wu, and Y. Ohyama, “Disturbance suppression for quadrotor UAV using sliding-mode-observer-based equivalent-input-disturbance approach,” ISA Transactions, vol. 92, pp. 286–297, February 2019.

    Article  Google Scholar 

  16. M. Labbadi, Y. Boukal, M. Taleb, and M. Cherkaoui, “Fractional order sliding mode control for the tracking problem of Quadrotor UAV under external disturbances,” Proc. of the European Control Conference, pp. 1595–1600, 2020.

  17. M. Labbadi, K. Boudaraia, A. Elakkary, M. Djemai, and M. Cherkaoui, “A continuous nonlinear sliding mode control with fractional operators for quadrotor UAV systems in the presence of disturbances,” Journal of Aerospace Engineering, vol. 35, no. 1, pp. 1–9, January 2022.

    Article  Google Scholar 

  18. M. Labbadi and H. E. Moussaoui, “An improved adaptive fractional-order fast integral terminal sliding mode control for distributed quadrotor,” Mathematics and Computers in Simulation, vol. 188, pp. 120–134, April 2021.

    Article  MATH  Google Scholar 

  19. K. Kondak, K. Krieger, A. Albu-Schaeffer, M. Schwarzbach, M. Laiacker, I. Maza, A. Rodriguez-Castano, and A. Ollero, “Closed-loop behavior of an autonomous helicopter equipped with a robotic arm for aerial manipulation tasks,” International Journal of Advanced Robotic Systems, vol. 10, no. 2, pp. 145–153, January 2013.

    Article  Google Scholar 

  20. G. Zhang, Y. He, B. Dai, F. Gu, J. Han, and G. Liu, “Robust control of an aerial manipulator based on a variable inertia parameters model,” IEEE Transactions on Industrial Electronics, vol. 67, no. 11, pp. 9515–9525, November 2020.

    Article  Google Scholar 

  21. M. Fanni and A. Khalifa, “A new 6-DOF quadrotor manipulation system: Design, kinematics, dynamics, and control,” IEEE/ASME Transactions on Mechatronics, vol. 22, no. 3, pp. 1315–1326, June 2017.

    Article  Google Scholar 

  22. S. Kim, S. Choi, H. Kim, J. Shin, H. Shim, and H. J. Kim, “Robust control of an equipment-added multirotor using disturbance observer,” IEEE Transactions on Control Systems Technology, vol. 26, no. 4, pp. 1524–1531, July 2018.

    Article  Google Scholar 

  23. J. Liang, Y. Chen, N. Lai, B. He, Z. Miao, and Y. Wang, “Low-complexity prescribed performance control for unmanned aerial manipulator robot system under model uncertainty and unknown disturbances,” IEEE Transactions on Industrial Informatics, vol. 18, no. 7, pp. 4632–4641, 2022.

    Article  Google Scholar 

  24. Y. Karayiannidis, D. Papageorgiou, and Z. Doulgeri, “A model-free controller for guaranteed prescribed performance tracking of both robot joint positions and velocities,” IEEE Robotics and Automation Letters, vol. 1, no. 1, pp. 267–273, January 2016.

    Article  Google Scholar 

  25. C. P. Bechlioulis, M. V. Liarokapis, and K. J. Kyriakopoulos, “Robust model free control of robotic manipulators with prescribed transient and steady state performance,” Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 41–46, 2014.

  26. M. Wang and A. Yang, “Dynamic learning from adaptive neural control of robot manipulators with prescribed performance,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 47, no. 8, pp. 2244–2255, August 2017.

    Article  Google Scholar 

  27. Y. Zhu, J. Qiao, and L. Guo, “Adaptive sliding mode disturbance observer-based composite control with prescribed performance of space manipulators for target capturing,” IEEE Transactions on Industrial Electronics, vol. 66, no. 3, pp. 1973–1983, March 2019.

    Article  Google Scholar 

  28. Q. Guo, Y. Zhang, B. G. Celler, and S. W. Su, “Neural adaptive backstepping control of a robotic manipulator with prescribed performance constraint,” IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 12, pp. 3572–3583, December 2019.

    Article  Google Scholar 

  29. N. A. Chaturvedi, A. K. Sanyal, and N. H. McClamroch, “Rigid-body attitude control,” IEEE Control Systems Magazine, vol. 31, no. 3, pp. 30–51, June 2011.

    Article  MATH  Google Scholar 

  30. H. Hua, Y. Fang, X. Zhang, and B. Lu, “A novel robust observer-based nonlinear trajectory tracking control strategy for quadrotors,” IEEE Transactions on Control Systems Technology, vol. 29, no. 5, pp. 1952–1963, September 2021.

    Article  Google Scholar 

  31. T. Lee, M. Leok, and N. H. McClamroch, “Geometric tracking control of a quadrotor UAV on SE(3),” Proc. of the 49th Conf. Decision and Control, pp. 5420–5425, 2010.

  32. J. Yang, S. Li, and X. Yu, “Sliding-mode control for systems with mismatched uncertainties via a disturbance observer,” IEEE Transactions on Industrial Electronics, vol. 60, no. 1, pp. 160–169, January 2013.

    Article  Google Scholar 

  33. D. Bainov and P. Simeonov, Integral Inequalities and Applications, Springer, New York, NY, USA, 1992.

    Book  MATH  Google Scholar 

  34. H. Khalil, Nonlinear Systems, 3rd ed., Prentice Hall, Englewood Cliffs, NJ, USA, 2002.

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Electrical and Information Engineering, Hunan University, Changsha, 410082, China

    Jiacheng Liang

  2. National Engineering Research Center of Robot Visual Perception and Control Technology, Changsha, 410082, China

    Jiacheng Liang

  3. School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, 350108, China

    Yanjie Chen, Ningbin Lai & Bingwei He

  4. Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, 200240, China

    Yanjie Chen

Authors
  1. Jiacheng Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yanjie Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ningbin Lai
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bingwei He
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yanjie Chen.

Additional information

Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This work was partially supported by National Natural Science Foundation of China (No. 62273098), Foundation of Key Laboratory of System Control and Information Processing, Ministry of Education, China (No. Scip202201).

Jiacheng Liang received his B.S. degree in mechanical design manufacture and automation and an M.S. degree in mechatronic engineering from Fuzhou University, Fuzhou, China, in 2019 and 2022, respectively. He is currently working toward the Ph.D. degree in control science and engineering from Hunan University, Changsha, China. His research interests include unmanned aerial manipulator robot, control theory, aerial physical interaction, and visual servoing.

Yanjie Chen received his B.S. degree in electrical engineering and its automation from Southwest Jiaotong University, Chengdu, China in 2011, his M.S. and Ph.D. degrees in control science and engineering from Hunan University, Changsha, China, in 2013 and 2017, respectively. From 2017 to 2021, he was an Assistant Professor with School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, China. He is currently an Associate Professor with School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, China. His current research interests include robotics, unmanned aerial manipulator, motion planning, and artificial intelligence.

Ningbin Lai received his B.S. degree in mechanical engineering and automation from Fuzhou University, Fuzhou, China in 2019. He is currently pursuing an M.S. degree in mechatronic engineering with the School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, China. His research interests include aerial manipulator robot and visual servoing.

Bingwei He received his B.S. degree in mechanical engineering from Yanshan University, China, in 1996, and his M.S. and Ph.D. degrees in mechanical engineering from Xi’an Jiaotong University, in 1999 and 2003, respectively. He is currently a Professor with the School of Mechanical Engineering and Automation, Fuzhou University. His research interests include intelligent mechanical and medical engineering.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liang, J., Chen, Y., Lai, N. et al. Robust Observer-based Trajectory Tracking Control for Unmanned Aerial Manipulator. Int. J. Control Autom. Syst. 21, 616–629 (2023). https://doi.org/10.1007/s12555-021-0829-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12555-021-0829-y

Keywords

Search

Navigation