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Finite-time composite learning control for nonlinear teleoperation systems under networked time-varying delays

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Abstract

The robust finite-time synchronization control problem is investigated for master-slave networked nonlinear telerobotics systems (NNTSs) in this article. Although there have been some research achievements on finite-time control for the NNTSs, these studies are based on the strong assumptions of communication time delays or can only achieve finite-time bounded convergence even when the external forces are zero. Accordingly and in view of the importance of these issues, a novel robust composite learning adaptive control scheme rendering the finite-time master-slave synchronization is proposed in this paper. In particular, the influence of time delays on finite-time convergence of the system is analyzed by employing the multi-dimension finite-time small-gain framework. Meanwhile, in order to achieve accurate and fast estimation of uncertain parameters of the system, both the online historical and the instantaneous data of the estimation data are explored to derive the new parameter adaptive law under a more realizable interval-excitation (IE) condition. Therefore, the convergence of the position/force synchronization errors and the adaptive parameter estimation errors is obtained in finite time, and enhanced robustness of the closed-loop system will also be ensured. Finally, the superior performance of the proposed control algorithms is validated by numerical simulations and hardware experiments.

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References

  1. Hokayem P F, Spong M W. Bilateral teleoperation: an historical survey. Automatica, 2006, 42: 2035–2057

    Article  MathSciNet  Google Scholar 

  2. Zhang B, Li H Y, Tang G J. Human control model in teleoperation rendezvous. Sci China Inf Sci, 2014, 57: 112205

    Article  Google Scholar 

  3. Kebria P M, Khosravi A, Nahavandi S, et al. Robust adaptive control scheme for teleoperation systems with delay and uncertainties. IEEE Trans Cybern, 2020, 50: 3243–3253

    Article  Google Scholar 

  4. Zhao Z H, Yang J, Liu C J, et al. Nonlinear composite bilateral control framework for n-DOF teleoperation systems with disturbances. Sci China Inf Sci, 2018, 61: 070221

    Article  MathSciNet  Google Scholar 

  5. Hua C C, Liu X P. Delay-dependent stability criteria of teleoperation systems with asymmetric time-varying delays. IEEE Trans Robot, 2010, 26: 925–932

    Article  Google Scholar 

  6. Yang Y N, Yan Y W, Hua C C, et al. Prescribed performance control for teleoperation system of nonholonomic constrained mobile manipulator without any approximation function. IEEE Trans Automat Sci Eng, 2023. 1–12

  7. Baranitha R, Mohajerpoor R, Rakkiyappan R. Bilateral teleoperation of single-master multislave systems with semi-markovian jump stochastic interval time-varying delayed communication channels. IEEE Trans Cybern, 2021, 51: 247–257

    Article  Google Scholar 

  8. Dong S L, Chen G R, Liu M Q, et al. Cooperative neural-adaptive fault-tolerant output regulation for heterogeneous nonlinear uncertain multiagent systems with disturbance. Sci China Inf Sci, 2021, 64: 172212

    Article  MathSciNet  Google Scholar 

  9. Zhao L, Yu J P, Wang Q G. Adaptive finite-time containment control of uncertain multiple manipulator systems. IEEE Trans Cybern, 2022, 52: 556–567

    Article  Google Scholar 

  10. Zhu Y P, Zhu W Y, Liu J P, et al. Command-filtered finite-time fuzzy adaptive fault-tolerant control of output-constrainted robotic manipulators with unknown dead-zones. IEEE Trans Circuits Syst II, 2023, 70: 2939–2943

    Google Scholar 

  11. Yang Y N, Hua C C, Guan X P. Adaptive fuzzy finite-time coordination control for networked nonlinear bilateral teleoperation system. IEEE Trans Fuzzy Syst, 2014, 22: 631–641

    Article  Google Scholar 

  12. Yang Y N, Hua C C, Guan X P. Finite time control design for bilateral teleoperation system with position synchronization error constrained. IEEE Trans Cybern, 2016, 46: 609–619

    Article  Google Scholar 

  13. Wang J W, Tian J W, Zhang X, et al. Control of time delay force feedback teleoperation system with finite time convergence. Front Neurorobot, 2022. doi: https://doi.org/10.3389/fnbot.2022.877069

  14. Zhai D H, Xia Y Q. Finite-time control of teleoperation systems with input saturation and varying time delays. IEEE Trans Syst Man Cybern Syst, 2017, 47: 1522–1534

    Article  Google Scholar 

  15. Wang Z W, Liang B, Sun Y C, et al. Adaptive fault-tolerant prescribed-time control for teleoperation systems with position error constraints. IEEE Trans Ind Inf, 2020, 16: 4889–4899

    Article  Google Scholar 

  16. Zhang H C, Song A G, Li H J, et al. Novel adaptive finite-time control of teleoperation system with time-varying delays and input saturation. IEEE Trans Cybern, 2021, 51: 3724–3737

    Article  Google Scholar 

  17. Bao J L, Wang H Q, Liu P X. Finite-time synchronization control for bilateral teleoperation systems with asymmetric time-varying delay and input dead zone. IEEE ASME Trans Mechatron, 2021, 26: 1570–1580

    Article  Google Scholar 

  18. Yang Y N, Hua C C, Li J P. A novel delay-dependent finite-time control of telerobotics system with asymmetric time-varying delays. IEEE Trans Contr Syst Technol, 2022, 30: 985–996

    Article  Google Scholar 

  19. Zhang H C, Song A G, Li H J, et al. Adaptive finite-time control scheme for teleoperation with time-varying delay and uncertainties. IEEE Trans Syst Man Cybern Syst, 2022, 52: 1552–1566

    Article  Google Scholar 

  20. Li L N, Liu Z X, Ma Z Q, et al. Adaptive neural learning finite-time control for uncertain teleoperation system with output constraints. J Intell Robot Syst, 2022, 105: 1–16

    Article  Google Scholar 

  21. Yang Y N, Hua C C, Li J P, et al. Finite-time output-feedback synchronization control for bilateral teleoperation system via neural networks. Inf Sci, 2017, 406–407: 216–233

    Article  Google Scholar 

  22. Yang Y N, Jiang H X, Gan L, et al. Fixed-time composite neural learning control of flexible telerobotic systems. IEEE Trans Cybern, 2023

  23. Yang Y N, Jiang H C, Hua C C, et al. Practical preassigned fixed-time fuzzy control for teleoperation system under scheduled shared-control framework. IEEE Trans Fuzzy Syst, 2023. 1–12

  24. Pan Y P, Yu H Y. Composite learning robot control with guaranteed parameter convergence. Automatica, 2018, 89: 398–406

    Article  MathSciNet  Google Scholar 

  25. Yang Y N, Hua C C, Li J P. Composite adaptive guaranteed performances synchronization control for bilateral teleoperation system with asymmetrical time-varying delays. IEEE Trans Cybern, 2022, 52: 5486–5497

    Article  Google Scholar 

  26. Na J, Xing Y S, Costa-Castello R. Adaptive estimation of time-varying parameters with application to roto-magnet plant. IEEE Trans Syst Man Cybern Syst, 2021, 51: 731–741

    Article  Google Scholar 

  27. Li Y L, Yin Y X, Zhang S, et al. Composite adaptive control for bilateral teleoperation systems without persistency of excitation. J Franklin Institute, 2020, 357: 773–795

    Article  MathSciNet  Google Scholar 

  28. Na J, Mahyuddin M N, Herrmann G, et al. Robust adaptive finite-time parameter estimation and control for robotic systems. Intl J Robust Nonlinear, 2020, 25: 3045–3071

    Article  MathSciNet  Google Scholar 

  29. Yang C G, Jiang Y M, He W, et al. Adaptive parameter estimation and control design for robot manipulators with finite-time convergence. IEEE Trans Ind Electron, 2018, 65: 8112–8123

    Article  Google Scholar 

  30. Zhang Y, Hua C C. Composite learning finite-time control of robotic systems with output constraints. IEEE Trans Ind Electron, 2023, 70: 1687–1695

    Article  Google Scholar 

  31. Makkar C, Hu G, Sawyer W G, et al. Lyapunov-based tracking control in the presence of uncertain nonlinear parameterizable friction. IEEE Trans Automat Contr, 2007, 52: 1988–1994

    Article  MathSciNet  Google Scholar 

  32. Bhat S P, Bernstein D S. Finite-time stability of continuous autonomous systems. SIAM J Control Optim, 2000, 38: 751–766

    Article  MathSciNet  Google Scholar 

  33. Abdessameud A, Polushin I G, Tayebi A. Synchronization of lagrangian systems with irregular communication delays. IEEE Trans Automat Contr, 2014, 59: 187–193

    Article  MathSciNet  Google Scholar 

  34. Hong Y G, Jiang Z P, Feng G. Finite-time input-to-state stability and applications to finite-time control design. SIAM J Control Optim, 2010, 48: 4395–4418

    Article  MathSciNet  Google Scholar 

  35. Hong Y G, Wang J K, Cheng D Z. Adaptive finite-time control of nonlinear systems with parametric uncertainty. IEEE Trans Automat Contr, 2006, 51: 858–862

    Article  MathSciNet  Google Scholar 

  36. Huang X Q, Lin W, Yang B. Global finite-time stabilization of a class of uncertain nonlinear systems. Automatica, 2018, 41: 881–888

    Article  MathSciNet  Google Scholar 

  37. Sanchez E, Alanis A. Adaptive approximation based control: unifying neural, fuzzy and traditional adaptive approximation approaches (Farrell J A, Polycarpou M M [Book review]). IEEE Trans Neural Netw, 2008, 19: 731–732

    Article  Google Scholar 

  38. Shen H H, Pan Y J. Improving tracking performance of nonlinear uncertain bilateral teleoperation systems with time-varying delays and disturbances. IEEE ASME Trans Mechatron, 2020, 25: 1171–1181

    Article  Google Scholar 

  39. Chen Z, Huang F H, Sun W C, et al. RBF-neural-network-based adaptive robust control for nonlinear bilateral teleoperation manipulators with uncertainty and time delay. IEEE ASME Trans Mechatron, 2020, 25: 906–918

    Article  Google Scholar 

  40. Zhang S, Yuan S, Yu X B, et al. Adaptive neural network fixed-time control design for bilateral teleoperation with time delay. IEEE Trans Cybern, 2022, 52: 9756–9769

    Article  Google Scholar 

  41. Xu J Z, Ge M F, Ling G, et al. Hierarchical predefined-time control of teleoperation systems with state and communication constraints. Intl J Robust Nonlinear, 2021, 31: 9652–9675

    Article  MathSciNet  Google Scholar 

  42. Bacha S C, Bai W B, Wang Z W, et al. Deep reinforcement learning-based control framework for multilateral telesurgery. IEEE Trans Med Robot BIon, 2022, 4: 352–355

    Article  Google Scholar 

  43. Wang Z W, Lam H K, Guo Y, et al. Adaptive event-triggered control for nonlinear systems with asymmetric state constraints: a prescribed-time approach. IEEE Trans Automat Contr, 2023, 68: 3625–3632

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

This work was supported in part by National Natural Science Foundation of China (Grant Nos. 62373319, 61933009, 62073276), Natural Science Foundation of Hebei Province (Grant Nos. F2022203036, F2021203109, F2021203054, F2022203025), and Provincial Key Laboratory Performance Subsidy Project (Grant No. 22567612H).

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Correspondence to Yana Yang.

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Yang, Y., Jiang, H., Hua, C. et al. Finite-time composite learning control for nonlinear teleoperation systems under networked time-varying delays. Sci. China Inf. Sci. 67, 162203 (2024). https://doi.org/10.1007/s11432-023-3931-0

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  • DOI: https://doi.org/10.1007/s11432-023-3931-0

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