面向双关节机械臂的参数可调RBF神经网络控制

刘凌 1; 李志成 1; 2; 张莹 1; 2

doi:10.7652/xjtuxb202104001

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面向双关节机械臂的参数可调RBF神经网络控制

更新时间：2025-07-09

- 面向双关节机械臂的参数可调RBF神经网络控制
- A RBF Neural Network Control Method with Adjustable Parameters for 2-Joint Robot Manipulators
- 西安交通大学学报 2021年55卷第4期页码：1-7
- 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：
  
  : 2020-11-02。作者简介: 刘凌(1980—),男,副教授,博士生导师。基金项目: 国家自然科学基金资助项目(51977173)
- DOI：10.7652/xjtuxb202104001
  中图分类号： TP183
- 网络首发：2021-04-10，
  
  纸质出版：2021
- 稿件说明：
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刘凌 1, 李志成 1, 2, 等. 面向双关节机械臂的参数可调RBF神经网络控制[J]. 西安交通大学学报, 2021,55(4):1-7.

A RBF Neural Network Control Method with Adjustable Parameters for 2-Joint Robot Manipulators[J]. 2021, 55(4): 1-7.
刘凌 1, 李志成 1, 2, 等. 面向双关节机械臂的参数可调RBF神经网络控制[J]. 西安交通大学学报, 2021,55(4):1-7. DOI： 10.7652/xjtuxb202104001.

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

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