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Neural control of uncertain robot manipulator with fixed-time convergence

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Abstract

In this paper, an adaptive NN (neural network) control scheme is proposed for uncertain robot systems to achieve fixed-time convergence. With the proposed fixed-time NN controller, the system uncertainty can be handled during the operation and the system can achieve semiglobal stability within fixed-time regardless of the initial conditions. In addition, the boundedness of the NNs weight estimation can be proved theoretically in our work, rather than being assumed as in some recent fixed-time NN control design. Finally, the superior control performance of the proposed scheme is demonstrated based on simulation and experiment study using a Baxter robot.

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Data Availability

The datasets generated during the current study are not publicly available due to the relevant regulations of the author’s institution but are available from the corresponding author on reasonable request.

References

  1. Kiang, C.T., Spowage, A., Yoong, C.K.: Review of control and sensor system of flexible manipulator. J. Intell. Robotic Syst. 77(1), 187–213 (2015)

    Article  Google Scholar 

  2. Righetti, L., Buchli, J., Mistry, M., Schaal, S.: Inverse dynamics control of floating-base robots with external constraints: A unified view. In: 2011 IEEE International Conference on Robotics and Automation, pp. 1085–1090 (2011). IEEE

  3. Kim, J.H., Hur, S.-m., Oh, Y.: \(l_1\) robustness of computed torque method for robot manipulators. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 330–335 (2018). IEEE

  4. Sun, J., He, H., Yi, J., Pu, Z.: Finite-time command-filtered composite adaptive neural control of uncertain nonlinear systems. IEEE Transactions Cybern. (2020)

  5. Huang, D., Yang, C., Pan, Y., Cheng, L.: Composite learning enhanced neural control for robot manipulator with output error constraints. IEEE Transactions Industrial Informatics 17(1), 209–218 (2019)

    Article  Google Scholar 

  6. Zerari, N., Chemachema, M.: Event-triggered adaptive output-feedback neural-networks control for saturated strict-feedback nonlinear systems in the presence of external disturbance. Nonlinear Dyn. 104(2), 1343–1362 (2021)

    Article  Google Scholar 

  7. Li, Y., Yang, T., Tong, S.: Adaptive neural networks finite-time optimal control for a class of nonlinear systems. IEEE Transactions Neural Netw. Learn. Syst. 31(11), 4451–4460 (2019)

    Article  MathSciNet  Google Scholar 

  8. Wang, L., He, H., Zeng, Z.: Global synchronization of fuzzy memristive neural networks with discrete and distributed delays. IEEE Transactions Fuzzy Syst. 28(9), 2022–2034 (2019)

    Article  Google Scholar 

  9. Vafaie, R.H., Mohammadzadeh, A., Piran, M., et al.: A new type-3 fuzzy predictive controller for mems gyroscopes. Nonlinear Dyn. 106(1), 381–403 (2021)

    Article  Google Scholar 

  10. Wang, L., He, H., Zeng, Z.: Intermittent stabilization of fuzzy competitive neural networks with reaction diffusions. IEEE Transactions Fuzzy Syst. 29(8), 2361–2372 (2020)

    Article  Google Scholar 

  11. Liu, L., Chen, A., Liu, Y.-J.: Adaptive fuzzy output-feedback control for switched uncertain nonlinear systems with full-state constraints. IEEE Transactions Cybern. (2021)

  12. Rao, H., Guo, Y., Xu, Y., Liu, C., Lu, R.: Nonfragile finite-time synchronization for coupled neural networks with impulsive approach. IEEE Transactions Neural Netw. Learn. Syst. 31(11), 4980–4989 (2020)

    Article  MathSciNet  Google Scholar 

  13. Sun, K., Qiu, J., Karimi, H.R., Gao, H.: A novel finite-time control for nonstrict feedback saturated nonlinear systems with tracking error constraint. IEEE Transactions Syst. Man Cybern. Syst. 51(6), 3968–3979 (2019)

    Article  Google Scholar 

  14. Zhang, H., Liu, Y., Dai, J., Wang, Y.: Command filter based adaptive fuzzy finite-time control for a class of uncertain nonlinear systems with hysteresis. IEEE Transactions Fuzzy Syst. 29(9), 2553–2564 (2020)

    Article  Google Scholar 

  15. Wang, L., Wang, H., Liu, P.X.: Adaptive fuzzy finite-time control of stochastic nonlinear systems with actuator faults. Nonlinear Dyn. 104(1), 523–536 (2021)

    Article  Google Scholar 

  16. Fu, C., Wang, Q.-G., Yu, J., Lin, C.: Neural network-based finite-time command filtering control for switched nonlinear systems with backlash-like hysteresis. IEEE Transactions Neural Netw. Learn. Syst. 32(7), 3268–3273 (2020)

    Article  MathSciNet  Google Scholar 

  17. Yang, C., Jiang, Y., Na, J., Li, Z., Cheng, L., Su, C.-Y.: Finite-time convergence adaptive fuzzy control for dual-arm robot with unknown kinematics and dynamics. IEEE Transactions Fuzzy Syst. 27(3), 574–588 (2018)

    Article  Google Scholar 

  18. Polyakov, A.: Nonlinear feedback design for fixed-time stabilization of linear control systems. IEEE Transactions Automatic Control 57(8), 2106–2110 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  19. Chen, M., Wang, H., Liu, X.: Adaptive fuzzy practical fixed-time tracking control of nonlinear systems. IEEE Transactions Fuzzy Syst. 29(3), 664–673 (2019)

    Article  Google Scholar 

  20. Yang, W., Wang, Y.-W., Morǎrescu, I.-C., Liu, X.-K., Huang, Y.: Fixed-time synchronization of competitive neural networks with multiple time scales. IEEE Transactions Neural Netw. Learn. Syst. (2021)

  21. Zhang, Y., Wang, F.: Observer-based fixed-time neural control for a class of nonlinear systems. IEEE Transactions Neural Netw. Learn. Syst. (2021)

  22. Chen, M., Wang, H., Liu, X.: Adaptive practical fixed-time tracking control with prescribed boundary constraints. IEEE Transactions Circuits Syst. I Regular P. 68(4), 1716–1726 (2021)

    Article  MathSciNet  Google Scholar 

  23. Mei, Y., Wang, J., Park, J.H., Shi, K., Shen, H.: Adaptive fixed-time control for nonlinear systems against time-varying actuator faults. Nonlinear Dyn. 107(4), 3629–3640 (2022)

    Article  Google Scholar 

  24. Li, B., Zhang, H., Xiao, B., Wang, C., Yang, Y.: Fixed-time integral sliding mode control of a high-order nonlinear system. Nonlinear Dyn. 107(1), 909–920 (2022)

    Article  Google Scholar 

  25. He, C., Wu, J., Dai, J., Zhe, Z., Tong, T.: Fixed-time adaptive neural tracking control for a class of uncertain nonlinear pure-feedback systems. IEEE Access 8, 28867–28879 (2020)

    Article  Google Scholar 

  26. Ba, D., Li, Y.-X., Tong, S.: Fixed-time adaptive neural tracking control for a class of uncertain nonstrict nonlinear systems. Neurocomputing 363, 273–280 (2019)

    Article  Google Scholar 

  27. Yang, W., Pan, Y., Liang, H.: Event-triggered adaptive fixed-time nn control for constrained nonstrict-feedback nonlinear systems with prescribed performance. Neurocomputing 422, 332–344 (2021)

    Article  Google Scholar 

  28. Lu, Z., Wang, N., Yang, C.: A novel iterative identification based on the optimised topology for common state monitoring in wireless sensor networks. Int. J. Syst. Sci. 53(1), 25–39 (2022)

    Article  MathSciNet  MATH  Google Scholar 

  29. Zhu, C., Jiang, Y., Yang, C.: Fixed-time parameter estimation and control design for unknown robot manipulators with asymmetric motion constraints. Int. J. Control Automation Syst. 20(1), 268–282 (2022)

    Article  Google Scholar 

  30. Cheng, L., Hou, Z.-G., Tan, M., Zhang, W.-J.: Tracking control of a closed-chain five-bar robot with two degrees of freedom by integration of an approximation-based approach and mechanical design. IEEE Transactions Syst. Man Cybern. Part B (Cybernetics) 42(5), 1470–1479 (2012)

  31. Huang, Y., Jia, Y.: Adaptive fixed-time six-dof tracking control for noncooperative spacecraft fly-around mission. IEEE Transactions Control Syst. Technol. 27(4), 1796–1804 (2018)

    Article  MATH  Google Scholar 

  32. Zhang, L., Wang, Y., Hou, Y., Li, H.: Fixed-time sliding mode control for uncertain robot manipulators. IEEE Access 7, 149750–149763 (2019)

    Article  Google Scholar 

  33. Yang, C., Jiang, Y., Li, Z., He, W., Su, C.-Y.: Neural control of bimanual robots with guaranteed global stability and motion precision. IEEE Transactions Industrial Informatics 13(3), 1162–1171 (2016)

    Article  Google Scholar 

  34. Prakash, R., Gupta, K.V., Behera, L.: Adaptive backstepping neural tracking control of an uncertain robot manipulator with dynamic disturbances. In: 2020 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1936–1943 (2020). IEEE

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Funding

This work was supported in part by National Nature Science Foundation of China (NSFC) under Grant U20A20200 and Major Research Grant No. 92148204, in part by Guangdong Basic and Applied Basic Research Foundation under Grants 2019B1515120076 and 2020B1515120054, in part by Industrial Key Technologies R & D Program of Foshan under Grant 2020001006308 and Grant 2020001006496.

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Author notes

  1. Chengzhi Zhu and Yiming Jiang have contributed equally.

Authors and Affiliations

  1. Key Laboratory of Autonomous Systems and Networked Control, College of Automation Science and Engineering, South China University of Technology, Wushan Road, Guangzhou, 510640, Guangdong, China

    Chengzhi Zhu & Chenguang Yang

  2. National Engineering Research Center for Robot Visual Perception and Control, Hunan University, Phoenix mountain street, Changsha, 410082, Hunan, China

    Yiming Jiang

Authors
  1. Chengzhi Zhu
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  2. Yiming Jiang
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  3. Chenguang Yang
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Contributions

All authors contributed to the study conception and design. Experiment preparation, data collection and analysis were performed by Chengzhi Zhu and Chenguang Yang. The first draft of the manuscript was written by Chengzhi Zhu and the manuscript polishing was done by Yiming Jiang. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Chenguang Yang.

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The authors have no relevant financial or non-financial interests to disclose.

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Zhu, C., Jiang, Y. & Yang, C. Neural control of uncertain robot manipulator with fixed-time convergence. Nonlinear Dyn 109, 849–861 (2022). https://doi.org/10.1007/s11071-022-07472-w

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