Adaptive-Neural-Network-Based Terminal Sliding Mode Control for Six-Axis Robotic Manipulators

被引：0

作者：

Jia H. ^{[1
,2
,3
]}

Liu Y. ^{[1
,2
,3
,4
]}

Wang Y. ^{[1
,2
,3
]}

Xue G. ^{[2
,3
,4
]}

机构：

[1] School of Mechanical Engineering, Shandong University, Jinan

[2] Key Laboratory of High-Efficiency and Clean Mechanical Manufacture of Ministry of Education, Shandong University, Jinan

[3] National Demonstration Center for Experimental Mechanical Engineering Education, Shandong University, Jinan

[4] Institute of Marine Science and Technology, Shandong University, Shandong, Qingdao

来源：

Hsi-An Chiao Tung Ta Hsueh/Journal of Xi'an Jiaotong University | 2022年 / 56卷 / 11期

关键词：

dynamic model; non-singular terminal sliding mode; RBF neural network; six-axis manipulator; trajectory tracking;

D O I：

10.7652/xjtuxb202211003

中图分类号：

学科分类号：

摘要：

This paper proposes an adaptive-neural-network-based terminal sliding mode control algorithm based on partitional approximation of dynamic models of robot manipulators to realize tracking of the desired trajectory of each joint generated by trajectory planning of six-axis robot manipulators when models are unknown and there is external disturbance. In addition, a non-singular terminal sliding mode manifold is proposed to accelerate the convergence of the tracking error and avoid the singular problem in the control with the conventional terminal sliding mode. Since the system model is unknown, multi-group RBF neural networks are utilized to approximate the dynamic model parameters, and the model is reconstructed by an adaptive weight update law. The model reconstruction errors are compensated by the designed robust terms. Simulation analysis is carried out in Simulink. According to the analysis results, compared with the RBF neural network global approximation algorithm, the proposed control method can reduce the maximum steady-state error of the manipulator joints by 83.7%. When the time-varying load is added to the end effector, the maximum steady-state error of the joints is reduced by 85.6%. Therefore, the proposed control is an effective and feasible trajectory tracking method for the six-axis robot manipulator under the condition of load variation. © 2022 Xi'an Jiaotong University. All rights reserved.

引用

页码：21 / 30

页数：9

共 26 条

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