A RBF Neural Network Control Method with Adjustable Parameters for 2-Joint Robot Manipulators

doi:10.7652/xjtuxb202104001

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A RBF Neural Network Control Method with Adjustable Parameters for 2-Joint Robot Manipulators

更新时间：2025-07-09

- A RBF Neural Network Control Method with Adjustable Parameters for 2-Joint Robot Manipulators
- Vol. 55, Issue 4, Pages: 1-7(2021)
- 作者机构：
  
  1. 西安交通大学电力设备电气绝缘国家重点实验室,西安,710049
  2. 西安交通大学电气工程学院,西安,710049
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202104001
  CLC： TP183
- Online First：10 April 2021，
  
  Published：2021
- 稿件说明：
移动端阅览
A RBF Neural Network Control Method with Adjustable Parameters for 2-Joint Robot Manipulators[J]. 2021, 55(4): 1-7.
DOI：

A RBF Neural Network Control Method with Adjustable Parameters for 2-Joint Robot Manipulators[J]. 2021, 55(4): 1-7. DOI： 10.7652/xjtuxb202104001.

摘要

为解决双关节机械臂轨迹控制中误差逼近过程初始误差大、达到稳态所需时间较长的问题

提出了一种面向双关节机械臂的新型参数可调径向基(RBF)神经网络控制方法。首先

利用梯度下降法对RBF神经网络中心参数进行迭代修正

该参数可以根据机械臂的实时误差进行调整

实现中心参数的在线优化; 进一步

提出了一种输入边界可以调整的模糊补偿器

该补偿器通过测量机械臂轨迹误差及误差的导数

经过模糊推理后将补偿器输出传递给转矩控制模块

从而使机械臂的输出转矩更接近理想值; 最后

采用遗传算法对RBF神经网络函数宽度值进行了寻优。仿真结果表明

采用参数可调的RBF神经网络控制方法对机械臂控制力矩进行调整后

机械臂控制过程中的精确度提高了59%

并且将机械臂轨迹跟踪的稳定时间缩短了69%。

Abstract

A new parameter-adjustable radial basis function(RBF)neural network control strategy for a double-joint manipulator is proposed to solve the problems of large initial error in the error approximation process and long time to reach the steady state in the trajectory control of the double-joint manipulator. Firstly

the central parameter of the RBF neural network is modified by a gradient descent method

so that the parameter can be adjusted according to the real-time error of the manipulator

and the online optimization of the parameter can be realized. A fuzzy compensator with adjustable input boundary is proposed to reach the goal that the actual trajectory of the manipulator approaches the ideal trajectory better. By measuring the trajectory error and the derivative of the error

the output of the compensator is transferred to the torque control module after fuzzy reasoning

so that the output torque of the manipulator is closer to the ideal value. Additionally

a genetic algorithm is used to optimize the width of the RBF neural network function. Simulation results show that after the control torque of the manipulator is adjusted by using the RBF neural network control method with adjustable parameters

the accuracy of the manipulator control process is improved by 59%

and the steady time of the manipulator trajectory tracking is shortened by 69%.

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references

KANGRU T, JURI R, MAHMOOD K, et al. Suitability analysis of using industrial robots in manufacturing [J]. Proceedings of the Estonian Academy of Sciences, 2019, 68(4): 383-388.

HUA Yu, GU Yongxia. Kinematics analysis of a robotic arm in a traffic cone automatic recovery and placement device [J]. Mechanical Engineering and Technology, 2020, 9(1): 1-12.

ARAD B, BALENDONCK J, BARTH R, et al. Development of a sweet pepper harvesting robot [J]. Journal of Field Robotics, 2020, 37(6): 1027-1039.

HIRZINGER G, BRUNNER B, LANDZETTEL K, et al. Space robotics: DLR's telerobotic concepts, lightweight arms and articulated hands [J]. Autonomous Robots, 2003, 14(2): 127-145.

SUN Ping, WANG Shuoyu, KARIMI H R. Robust redundant input reliable tracking control for omnidirectional rehabilitative training walker [J]. Mathematical Problems in Engineering, 2014, 2014: 636934.

DENG Yongting, WANG Jianlin, LI Hongwei, et al. Adaptive sliding mode current control with sliding mode disturbance observer for PMSM drives [J]. ISA Transactions, 2019, 88: 113-126.

MA Zhiqiang, SUN Guanghui. Dual terminal sliding mode control design for rigid robotic manipulator [J]. Journal of the Franklin Institute, 2017, 355(18): 9127-9149.

QURESHI M S, SWARNKAR P, GUPTA S. A supervisory on-line tuned fuzzy logic based sliding mode control for robotics: an application to surgical robots [J]. Robotics and Autonomous Systems, 2018, 109: 68-85.

WANG Yaoyao, YAN Fei, JIANG Surong, et al. Adaptive nonsingular terminal sliding mode control of cable-driven manipulators with time delay estimation [J]. International Journal of Systems Science, 2020, 51(1): 1-19.

ZHOU Xingyu, WANG Haoping, TIAN Yang, et al. Disturbance observer-based adaptive boundary iterative learning control for a rigid-flexible manipulator with input backlash and endpoint constraint [J]. International Journal of Adaptive Control and Signal Processing, 2020, 34(9): 1220-1241.

ZHAO Lin, JIA Yingmin, YU Jinpeng. Adaptive finite-time bipartite consensus for second-order multi-agent systems with antagonistic interactions [J]. Systems and Control Letters, 2017, 102: 22-31.

YANG Chenguang, TENG Tao, XU Bin, et al. Global adaptive tracking control of robot manipulators using neural networks with finite-time learning convergence [J]. International Journal of Control, Automation and Systems, 2017, 15(11): 1916-1924.

杨航, 刘凌, 倪骏康, 等. 双关节刚性机器人自适应BP神经网络算法 [J]. 西安交通大学学报, 2018, 52(1): 129-135.

YANG Hang, LIU Ling, NI Junkang, et al. An adaptive BP neural network algorithm for 2-joint rigid robots [J]. Journal of Xi'an Jiaotong University, 2018, 52(1): 129-135.

VALLURU S, SINGH M. Optimization strategy of bio-inspired metaheuristic algorithms tuned PID controller for PMBDC actuated robotic manipulator [J]. Procedia Computer Science, 2020, 171: 2040-2049.

许洋洋, 王莹, 薛东彬. 机械臂神经网络控制优化与仿真 [J]. 中国工程机械学报, 2018, 16(5): 416-420.

XU Yangyang, WANG Ying, XUE Dongbin. Neural network control optimization and simulation of robot arm [J]. Chinese Journal of Construction Machinery, 2018, 16(5): 416-420.

LEWIS F, LIU K, YESILDIREK A. Neural net robot controller with guaranteed tracking performance [J]. IEEE Transactions on Neural Networks, 1995, 6(3): 703-715.

刘金琨. 机器人控制系统的设计与MATLAB仿真 [M]. 北京: 清华大学出版社, 2008: 102-112.

ZHAO Dongya, NI Wei, ZHU Quanmin. A framework of neural networks based consensus control for multiple robotic manipulators [J]. Neurocomputing, 2014, 140: 8-18.

WANG Yuqi, LIN Qi, WANG Xiaoguang, et al. Adaptive PD control based on RBF neural network for a wire-driven parallel robot and prototype experiments [J]. Mathematical Problems in Engineering, 2019, 2019: 6478506.

房德君. 基于熵聚类RBF神经网络的机械臂轨迹跟踪控制 [J]. 机械设计与制造, 2017(10): 32-35.

FANG Dejun. Manipulator trajectory tracking control based on entropy clustering RBF neural network [J]. Machinery Design Manufacture, 2017(10): 32-35.

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