Trajectory tracking of robot manipulator with adaptive fuzzy second-order super-twisting sliding mode control

被引：1

作者：

Zhu D. ^{[1
]}

Zhu P. ^{[2
]}

He Y. ^{[1
]}

机构：

[1] School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou

[2] Department of Mechanical and Automation Engineering, The Chinese University of Hongkong

来源：

Advanced Robotics | 2023年 / 37卷 / 22期

关键词：

adaptive fuzzy super-twisting algorithm; Robot manipulator; second-order sliding mode control; trajectory tracking;

D O I：

10.1080/01691864.2023.2270792

中图分类号：

学科分类号：

摘要：

To solve the influence of uncertainties such as unmodeled errors and external disturbances on the trajectory tracking accuracy of the end-effector of a robot manipulator, a novel fuzzy super-twisting second-order sliding mode control method is proposed in this paper. Based on the dynamic model of the robot manipulator, a second-order sliding mode control algorithm is proposed by using the super-twisting to determine the non-singular terminal sliding manifold. An adaptive fuzzy algorithm is presented to compensate for the super-twisting second-order sliding mode control system for handling the chattering and overestimating the controller gains. The stability of the proposed controller is verified by the Lyapunov stability theory. Simulation and experimental results show that the proposed control method can enable the robot to track the trajectory accurately under complex and uncertain conditions and effectively suppress the chattering phenomenon of the system. © 2023 Informa UK Limited, trading as Taylor & Francis Group and The Robotics Society of Japan.

引用

页码：1438 / 1445

页数：7

共 32 条

[1]

Cheng J., Bi S.S., Yuan C., A graph theory-based method for dynamic modeling and parameter identification of 6-DOF industrial robots, Appl Sci Basel, 11, 22, (2021)

[2]

Gracia L., Solanes J.E., Munoz-Benavent P., Adaptive sliding mode control for robotic surface treatment using force feedback, Mechatronics, 52, pp. 102-118, (2018)

[3]

Zhen S.C., Ma M.C., Liu X.L., Model-based robust control design and experimental validation of SCARA robot system with uncertainty, J Vib Control, 29, 1-2, pp. 91-104, (2022)

[4]

Peng G.Z., Yang C.G., He W., Force sensorless admittance control with neural learning for robots with actuators saturation, IEEE Trans Indust Electron, 67, 4, pp. 3138-3148, (2020)

[5]

Negrean I., Crisan A., Formulations on accuracy in advanced robot mechanics, Acta Technica Napocensis Ser Appl Meth Mechan Eng, 62, 1, pp. 23-32, (2019)

[6]

Zhao L., Zhao X.H., Li B., Nonlinear friction dynamic modeling and performance analysis of flexible parallel robot, Int J Adv Robot Syst, 17, 6, (2020)

[7]

Mei Z.W., Chen L.P., Ding J.W., Feed-forward control of elastic-joint industrial robot based on hybrid inverse dynamic model, Adv Mechan Eng, 13, 9, (2021)

[8]

Dai L., Yu Y.T., Zhai D.H., Robust model predictive tracking control for robot manipulators with disturbances, IEEE Trans Indust Electron, 68, 5, pp. 4288-4297, (2012)

[9]

Zhang J.J., Fang Z.L., Zhang Z.Q., Trajectory tracking control of nonholonomic wheeled mobile robots using model predictive control subjected to Lyapunov-based input constraints, Int J Control Automat Syst, 20, 5, pp. 1640-1651, (2022)

[10]

Chen S.Y., Wen J.T., Industrial robot trajectory tracking control using multi-layer neural networks trained by iterative learning control, Robotics, 10, 1, (2021)

← 1 2 3 4 →