Research article

Nonlinear extended state observer based control for the teleoperation of robotic systems with flexible joints

  • Received: 07 September 2023 Revised: 15 December 2023 Accepted: 20 December 2023 Published: 25 December 2023
  • The control of robot manipulator pose is significantly complicated by the uncertainties arising from flexible joints, presenting substantial challenges in incorporating practical operational constraints. These challenges are further exacerbated in teleoperation scenarios, where factors such as synchronization and external disturbances further amplify the difficulties. At the core of this research is the introduction of a pioneering teleoperation controller, ingeniously integrating a nonlinear extended state observer (ESO) with the barrier Lyapunov function (BLF) while effectively accommodating a steady time delay. The controller in our study demonstrates exceptional proficiency in accurately estimating uncertainties arising from both flexible joints and external disturbances using the nonlinear ESO. Refined estimates, in conjunction with operational constraints of the system, are integrated into our BLF-based controller. Consequently, a synchronized control mechanism for teleoperation is achieved, exhibiting promising performance. Importantly, our experimental findings provide substantial evidence that our proposed approach effectively reduces the tracking error of the teleoperation system to within 0.02 rad. This advancement highlights the potential of our controller in significantly enhancing the precision and reliability of teleoperated robot manipulators.

    Citation: Yongli Yan, Fucai Liu, Teng Ren, Li Ding. Nonlinear extended state observer based control for the teleoperation of robotic systems with flexible joints[J]. Mathematical Biosciences and Engineering, 2024, 21(1): 1203-1227. doi: 10.3934/mbe.2024051

    Related Papers:

  • The control of robot manipulator pose is significantly complicated by the uncertainties arising from flexible joints, presenting substantial challenges in incorporating practical operational constraints. These challenges are further exacerbated in teleoperation scenarios, where factors such as synchronization and external disturbances further amplify the difficulties. At the core of this research is the introduction of a pioneering teleoperation controller, ingeniously integrating a nonlinear extended state observer (ESO) with the barrier Lyapunov function (BLF) while effectively accommodating a steady time delay. The controller in our study demonstrates exceptional proficiency in accurately estimating uncertainties arising from both flexible joints and external disturbances using the nonlinear ESO. Refined estimates, in conjunction with operational constraints of the system, are integrated into our BLF-based controller. Consequently, a synchronized control mechanism for teleoperation is achieved, exhibiting promising performance. Importantly, our experimental findings provide substantial evidence that our proposed approach effectively reduces the tracking error of the teleoperation system to within 0.02 rad. This advancement highlights the potential of our controller in significantly enhancing the precision and reliability of teleoperated robot manipulators.



    加载中


    [1] G. Li, F. Caponetto, E. Del Bianco, V. Katsageorgiou, I. Sarakoglou, N. G. Tsagarakis, A workspace limit approach for teleoperation based on signed distance function, IEEE Rob. Autom. Lett., 6 (2021), 5589–5596. https://doi.org/10.1109/LRA.2021.3079810 doi: 10.1109/LRA.2021.3079810
    [2] M. S. Mahmoud, M. Maaruf, Prescribed performance output feedback synchronisation control of bilateral teleoperation system with actuator nonlinearities, Int. J. Syst. Sci., 52 (2021), 3115–3127. https://doi.org/10.1080/00207721.2021.1921308 doi: 10.1080/00207721.2021.1921308
    [3] Z. Deng, S. Zhang, Y. Guo, H. Jiang, X. Zheng, B. He, Assisted teleoperation control of robotic endoscope with visual feedback for nasotracheal intubation, Rob. Auton. Syst., 172 (2024), 104586. https://doi.org/10.1016/j.robot.2023.104586 doi: 10.1016/j.robot.2023.104586
    [4] M. Azeez, A. A. Abdelhaleem, S. Elnaggar, K. A. F. Moustafa, K. R. Atia, Optimized sliding mode controller for trajectory tracking of flexible joints three-link manipulator with noise in input and output, Sci. Rep., 13 (2023), 12518. https://doi.org/10.1038/s41598-023-38855-7 doi: 10.1038/s41598-023-38855-7
    [5] M. Shi, J. Yu, T. Zhang, Command filter‐based adaptive control of flexible‐joint manipulator with input saturation and output constraints, Asian J. Control, 2023 (2023), forthcoming. https://doi.org/10.1002/asjc.3177 doi: 10.1002/asjc.3177
    [6] J. Reinecke, A. Dietrich, A. Shu, B. Deutschmann, M. Hutter, A robotic torso joint with adjustable linear spring mechanism for natural dynamic motions in a differential-elastic arrangement, IEEE Rob. Autom. Lett., 7 (2022), 9–16. https://doi.org/10.1109/LRA.2021.3117245 doi: 10.1109/LRA.2021.3117245
    [7] E. Spyrakos-Papastavridis, J. S. Dai, Minimally model-based trajectory tracking and variable impedance control of flexible-joint robots, IEEE Trans. Ind. Electron., 68 (2020), 6031–6041. https://doi.org/10.1109/TIE.2020.2994886 doi: 10.1109/TIE.2020.2994886
    [8] N. Kashiri, J. Lee, N. G. Tsagarakis, M. Van Damme, B. Vanderborght, D. G. Caldwell, Proxy-based position control of manipulators with passive compliant actuators: Stability analysis and experiments, Rob. Auton. Syst., 75 (2016), 398–408. https://doi.org/10.1016/j.robot.2015.09.003 doi: 10.1016/j.robot.2015.09.003
    [9] M. W. Spong, Modeling and control of elastic joint robots, J. Dyn. Sys., Meas. Control., 109 (1987), 310–318. https://doi.org/10.1115/1.3143860 doi: 10.1115/1.3143860
    [10] M. W. Spong, Adaptive control of flexible joint manipulators: Comments on two papers, Automatica, 31 (1995), 585–590. https://doi.org/10.1016/0005-1098(95)98487-Q doi: 10.1016/0005-1098(95)98487-Q
    [11] F. Ghorbel, J. Y. Hung, M. W. Spong, Adaptive control of flexible-joint manipulators, IEEE Control Syst. Mag., 9 (1989), 9–13. https://doi.org/10.1109/37.41450 doi: 10.1109/37.41450
    [12] Y. Z. Chang, R. W. Daniel, On the adaptive control of flexible joint robots, Automatica, 28 (1992), 969–974. https://doi.org/10.1016/0005-1098(92)90149-A doi: 10.1016/0005-1098(92)90149-A
    [13] M. Hong, X. Gu, L. Liu, Y. Guo, Finite time extended state observer based nonsingular fast terminal sliding mode control of flexible-joint manipulators with unknown disturbance. J. Franklin Inst., 360 (2023), 18–37. https://doi.org/10.1016/j.jfranklin.2022.10.028 doi: 10.1016/j.jfranklin.2022.10.028
    [14] D. P. Nam, P. T. Loc, N. V. Huong, D. T. Tan, A finite-time sliding mode controller design for flexible joint manipulator systems based on disturbance observer, Int. J. Mech. Eng. Rob. Res., 8 (2019), 619–625. https://doi.org/10.18178/ijmerr.8.4.619-625 doi: 10.18178/ijmerr.8.4.619-625
    [15] J. W. Huang, J. S. Lin, Backstepping control design of a single-link flexible robotic manipulator, IFAC Proceed. Vol., 41 (2008), 11775–11780. https://doi.org/10.3182/20080706-5-KR-1001.01994 doi: 10.3182/20080706-5-KR-1001.01994
    [16] X. Cheng, Y. Zhang, H. Liu, D. Wollherr, M. Buss, Adaptive neural backstepping control for flexible-joint robot manipulator with bounded torque inputs, Neurocomputing, 458 (2021), 70–86. https://doi.org/10.1016/j.neucom.2021.06.013 doi: 10.1016/j.neucom.2021.06.013
    [17] U. K. Sahu, B. Subudhi, D. Patra, Sampled-data extended state observer-based backstepping control of two-link flexible manipulator, Trans. Inst. Measure. Control, 41 (2019), 3581–3599. https://doi.org/10.1177/0142331219832954 doi: 10.1177/0142331219832954
    [18] J. Han, From PID to active disturbance rejection control, IEEE Trans. Ind. Electron., 56 (2009), 900–906. https://doi.org/10.1109/TIE.2008.2011621 doi: 10.1109/TIE.2008.2011621
    [19] Y. Yan, L. Ding, Y. Yang, F. Liu, Discrete sliding mode control design for bilateral teleoperation system via adaptive extended state observer, Sensors, 20 (2020), 5091. https://doi.org/10.3390/s20185091 doi: 10.3390/s20185091
    [20] Y. Xia, M. Fu, C. Li, F. Pu, Y. Xu, Active disturbance rejection control for active suspension system of tracked vehicles with gun, IEEE Trans. Ind. Electron., 65 (2018), 4051–4060. https://doi.org/10.1109/TIE.2017.2772182 doi: 10.1109/TIE.2017.2772182
    [21] A. A. Najm, I. K. Ibraheem, A. T. Azar, A. J. Humaidi, On the stabilization of 6-DOF UAV quadrotor system using modified active disturbance rejection control, in Unmanned Aerial Systems, (2021), 257–287. https://doi.org/10.1016/B978-0-12-820276-0.00018-2
    [22] X. Zhou, Q. Liu, Y. Ma, B. Xie, DC-link voltage research of photovoltaic grid-connected inverter using improved active disturbance rejection control, IEEE Access, 9 (2021), 9884–9894. https://doi.org/10.1109/ACCESS.2021.3050191 doi: 10.1109/ACCESS.2021.3050191
    [23] M. Li, J. Zhao, Y. Hu, Z. Wang, Active disturbance rejection position servo control of PMSLM based on reduced-order extended state observer, Chin. J. Electr. Eng., 6 (2020), 30–41. https://doi.org/10.23919/CJEE.2020.000009 doi: 10.23919/CJEE.2020.000009
    [24] Z. L. Zhao, B. Z. Guo, A nonlinear extended state observer based on fractional power functions, Automatica, 81 (2017), 286–296. https://doi.org/10.1016/j.automatica.2017.03.002 doi: 10.1016/j.automatica.2017.03.002
    [25] D. Mu, L. Li, G. Wang, Y. Fan, Y. Zhao, X. Sun, State constrained control strategy for unmanned surface vehicle trajectory tracking based on improved barrier Lyapunov function, Ocean Eng., 277 (2023), 114276. https://doi.org/10.1016/j.oceaneng.2023.114276 doi: 10.1016/j.oceaneng.2023.114276
    [26] C. Wang, Y. Wu, J. Yu, Barrier Lyapunov functions-based adaptive control for nonlinear pure-feedback systems with time-varying full state constraints, Int. J. Control Autom. Syst., 15 (2017), 2714–2722. https://doi.org/10.1007/s12555-016-0321-2 doi: 10.1007/s12555-016-0321-2
    [27] J. Li, Y. J. Liu, Control of nonlinear systems with full state constraints using integral Barrier Lyapunov Functionals, in 2015 International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS), 2015, 66–71. https://doi.org/10.1109/ICCSS.2015.7281151
    [28] S. Zhang, M. Lei, Y. Dong, W. He, Adaptive neural network control of coordinated robotic manipulators with output constraint, IET Control Theory Appl., 10 (2016), 2271–2278. https://doi.org/10.1049/iet-cta.2016.0009 doi: 10.1049/iet-cta.2016.0009
    [29] X. Yu, W. He, H. Li, J. Sun, Adaptive fuzzy full-state and output-feedback control for uncertain robots with output constraint, IEEE Trans. Syst. Man Cybern. Syst., 51 (2020), 6994–7007. https://doi.org/10.1109/TSMC.2019.2963072 doi: 10.1109/TSMC.2019.2963072
  • Reader Comments
  • © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(925) PDF downloads(63) Cited by(1)

Article outline

Figures and Tables

Figures(10)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog