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
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
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)
Rayankula V, Pathak PM (2021) Fault tolerant control and reconfiguration of mobile manipulator. J Intell Robot Syst 101:34
Yang M, Zhang YN, Hu HF (2021) Posture coordination control of two-manipulator system using projection neural network. Neurocomputing 427:179–190
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
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
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
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
Xiao B, Yin S (2019) Exponential tracking control of robotic manipulators with uncertain dynamics and kinematics. IEEE Trans Indus Inform 15(2):689–698
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
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
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
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
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
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
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
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
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
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
Wang SS, Tuo YL (2020) Robust trajectory tracking control of underactuated surface vehicles with prescribed performance. Polish Maritime Res 27(4):148–156
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
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
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
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
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
Liu JK (2016) Robot control system design and Matlab simulation the basic design method. Tsinghua University Press, Beijing
Liu JK (2017) Robot control system design and Matlab simulation the advanced design method. Tsinghua University Press, Beijing
Gao HL, Zhang HC, Li XL (2021) Sliding mode control of the vehicle speed system based on LMIs. Complexity 2021:1–8
Andreev A, Peregudova O (2019) On global trajectory tracking control of robot manipulators in cylindrical phase space. Int J Control 93(12):3003–3015
Liu Q, Li DY, Ge SZ et al (2021) Adaptive bias RBF neural network control for a robotic manipulator. Neurocomputing 447:213–223
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
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
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
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
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
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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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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DOI: https://doi.org/10.1007/s11063-023-11237-w