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A Novel Adaptive Sliding Mode Control of Robot Manipulator Based on RBF Neural Network and Exponential Convergence Observer

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Abstract

This paper focuses on a novel adaptive sliding mode control (NASMC) of robot manipulator based on RBF (radial basis function) neural network and observer. A novel adaptive sliding mode control can achieve high performance tracking control by designing three adaptive parameters. Different from other existing adaptive control methods, an exponential convergence observer is designed to solve the parameter uncertainty, and the unknown nonlinear friction can be obtained by the online estimation of RBF neural network. Then the observer value and the RBF neural network estimation value are transferred to the controller, and the equivalent compensation is introduced to realize the stable control of the system. By utilizing Lyapunov stability theory, it is proved that the system can realize adaptive control under the designed controller. The effectiveness of the control method is verified by simulation. The amount of operation can be reduced through the NASMC method, and the value of root mean squared error is 0.00031795, which is closer to 0. Compared with adaptive sliding mode control (ASMC) and RBF neural network adaptive control (RBFAC), the robot manipulator system has better tracking effect.

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References

  1. Bostelman R, Foufou S, Hong T et al (2017) Model of mobile manipulator performance measurement using SysML. J Intell Robot Syst 92(1):65–83

    Article  Google Scholar 

  2. Yu HY, Li XL, Yao M, et al. (2021) Design and analysis of positioning manipulator structure for vascular interventional surgery robot system. J Phys Conf Ser, 1906(1)

  3. Rayankula V, Pathak PM (2021) Fault tolerant control and reconfiguration of mobile manipulator. J Intell Robot Syst 101:34

    Article  Google Scholar 

  4. Yang M, Zhang YN, Hu HF (2021) Posture coordination control of two-manipulator system using projection neural network. Neurocomputing 427:179–190

    Article  Google Scholar 

  5. Chang ZL, Hao LZ, Yan QY et al (2021) Research on manipulator tracking control algorithm based on RBF neural network. J Phys Conf Ser 1802:032072

    Article  Google Scholar 

  6. Yao QJ (2020) Adaptive trajectory tracking control of a free-flying space robot subject to input nonlinearities. J Brazilian Soc Mech Sci Eng 42:574

    Article  Google Scholar 

  7. Tran DT, Jin M, Ahn KK (2019) Nonlinear extended state observer based on output feedback control for a manipulator with time-varying output constraints and external disturbance. IEEE Access 7:156860–156870

    Article  Google Scholar 

  8. Shafei A, Mirzaeinejad H (2021) A novel recursive formulation for dynamic modeling and trajectory tracking control of multi-rigid-link robotic manipulators mounted on a mobile platform. Proc Inst Mech Eng Part I J Syst Control Eng 235(7):1204–1217

    Google Scholar 

  9. Xiao B, Yin S (2019) Exponential tracking control of robotic manipulators with uncertain dynamics and kinematics. IEEE Trans Indus Inform 15(2):689–698

    Article  MathSciNet  Google Scholar 

  10. Vo AT, Kang H, Nguyen V (2017) An output feedback tracking control based on neural sliding mode and high order sliding mode observer. In: 2017 10th international conference on human system interactions (HSI), pp 161–165

  11. Shi DN, Zhang JH, Sun ZQ, et al. (2021) Composite trajectory tracking control for robot manipulator with active disturbance rejection. Control Eng Pract 106: 104670

  12. Cheng GL, Yuan J (2018) Disturbance observer based composite nonlinear feedback controller design for robot manipulators. In: 2018 IEEE international conference of intelligent robotic and control engineering (IRCE), pp 14-18

  13. Pan RC, Li ZG (2021) Adaptive sliding mode control of projectile coordination arm based on disturbance observer. J Ordnance Equip Eng 42(4):53–57

    Google Scholar 

  14. Ni J, Shi H, Wang M (2020) Disturbance observer-based cooperative learning tracking control for multi-manipulators. In: 2020 7th international conference on information, cybernetics, and computational social systems (ICCSS), pp 229-234

  15. Yahia R, Gritli H, Khraief N, Belghith S (2018) Robust control of a robotic manipulator using LMI-based high-gain state and disturbance observers. In: 2018 15th international multi-conference on systems, signals & devices (SSD), pp 1190–1196

  16. Huang Y, Cheng L, Li Z et al. (2019) Backstepping sliding mode control for robot manipulator via nonlinear disturbance observer. In: 2019 Chinese control conference (CCC), pp 3220–3224

  17. Li Q, Gao Y, Ti B, Zhao J (2019) Model-error-observer-based control of robotic manipulator with uncertain dynamics. In: 2019 IEEE 2nd international conference on information and computer technologies (ICICT), pp 255-260

  18. Mustafa A, Dhar NK, Agrawal P, Yerma NK (2017) Adaptive backstepping sliding mode control based on nonlinear disturbance observer for trajectory tracking of robotic manipulator. In: 2017 2nd international conference on control and robotics engineering (ICCRE), pp 29-34

  19. Zheng W, Chen M, Zhu R, Mei R (2019) Tracking control of two DOF manipulator based on LADRC. In: 2019 IEEE 4th international conference on advanced robotics and mechatronics (ICARM), pp220–225

  20. Wang SS, Tuo YL (2020) Robust trajectory tracking control of underactuated surface vehicles with prescribed performance. Polish Maritime Res 27(4):148–156

    Article  Google Scholar 

  21. Fan K, Liu Y, Bian G (2020) Improved sliding mode control based on disturbance observer for robot assisted surgery training. In: 2020 Chinese automation congress (CAC), pp 4429-4434

  22. Yu L, Huang J (2018) Sliding mode switching control scheme for an uncertain robotic manipulator system under arbitrary switchings. In: 2018 33rd youth academic annual conference of Chinese association of automation (YAC), pp 239-542

  23. Liu H, Sun J, Nie J, Chen G, Zou L (2019) Adaptive non-singular terminal sliding mode control with high-gain observers for robotic manipulators. In: 2019 Chinese control and decision conference (CCDC), pp 3547-3552

  24. Nguyen V, Vo A, Kang H (2020) A non-singular fast terminal sliding mode control based on third-order sliding mode observer for a class of second-order uncertain nonlinear systems and its application to robot manipulators. IEEE Access 8:78109–78120

    Article  Google Scholar 

  25. Ji N, Liu JK, Yang HJ (2020) Sliding mode control based on RBF neural network for a class of underactuated systems with unknown sensor and actuator faults. Int J Syst Sci 51(16):3539–3549

    Article  MathSciNet  MATH  Google Scholar 

  26. Liu JK (2016) Robot control system design and Matlab simulation the basic design method. Tsinghua University Press, Beijing

    Google Scholar 

  27. Liu JK (2017) Robot control system design and Matlab simulation the advanced design method. Tsinghua University Press, Beijing

    Google Scholar 

  28. Gao HL, Zhang HC, Li XL (2021) Sliding mode control of the vehicle speed system based on LMIs. Complexity 2021:1–8

    Google Scholar 

  29. Andreev A, Peregudova O (2019) On global trajectory tracking control of robot manipulators in cylindrical phase space. Int J Control 93(12):3003–3015

    Article  MathSciNet  MATH  Google Scholar 

  30. Liu Q, Li DY, Ge SZ et al (2021) Adaptive bias RBF neural network control for a robotic manipulator. Neurocomputing 447:213–223

    Article  Google Scholar 

  31. Shang DY, Li XP, Yin M et al (2021) Control method of flexible manipulator servo system based on a combination of RBF neural network and pole placement strategy. Mathematics 9(8):896–896

    Article  Google Scholar 

  32. Zhang Y, Kim D, Zhao Y et al (2020) PD control of a manipulator with gravity and inertia compensation using an RBF neural network. Int J Control Autom Syst 18:3083–3092

    Article  Google Scholar 

  33. Xu FX, Tang DQ, Wang SS (2020) Research on parallel nonlinear control system of PD and RBF neural network based on U model. Automatika 61(2):284–294

    Article  Google Scholar 

  34. Sun YG, Xu JQ, Qiang HY et al (2019) Adaptive sliding mode control of maglev system based on RBF neural network minimum parameter learning method. Measurement 141:217–226

    Article  Google Scholar 

  35. Gao HL, Li XL, Gao C, Wu J (2021) Neural network supervision control strategy for inverted pendulum tracking control. Discrete Dyn Nat Soc 2021:1–14

    MathSciNet  MATH  Google Scholar 

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 61602163), the Provincial Teaching and Research Projects of Higher Education Institutions in Hubei Province (Grant No.2020602), and the Natural Science Foundation of Hubei Province (Grant No.2021CFB578).

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Authors and Affiliations

  1. School of Electrical Engineering and Automation, Hubei Normal University, Huangshi, 435002, China

    Xiaoling Li, Hongliang Gao, Lang Xiong, Hongcong Zhang & Bing Li

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  1. Xiaoling Li
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  2. Hongliang Gao
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  3. Lang Xiong
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  4. Hongcong Zhang
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  5. Bing Li
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Correspondence to Hongliang Gao.

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Li, X., Gao, H., Xiong, L. et al. A Novel Adaptive Sliding Mode Control of Robot Manipulator Based on RBF Neural Network and Exponential Convergence Observer. Neural Process Lett 55, 10037–10052 (2023). https://doi.org/10.1007/s11063-023-11237-w

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