基于模糊自适应滑模算法的多机械臂力/位混合控制
作者:
作者单位:

作者简介:

通讯作者:

中图分类号:

基金项目:

国家自然科学基金项目(61973167);盐城工学院校级科研项目(xjr2020041);江苏省产学研合作项目(BY2022495);教育部产学合作协同育人项目(22097110113736)


Force/Position Hybrid Control of Multi-manipulator Based on Fuzzy Adaptive Sliding Mode Algorithm
Author:
Affiliation:

Fund Project:

This work is supported by National Natural Science Foundation of China (61973167), School Level Scientific Research Project of Yancheng Institute of Technology (xjr2020041), Jiangsu Province Industry-University-Research Cooperation Project (BY2022495), and Cooperative Education Project of the Ministry of Education (22097110113736)

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    针对多机械臂力/位混合控制受摩擦力等不确定因素影响的问题,该文提出了一种基于模糊自适应鲁棒滑模算法的控制方法。首先,根据机械臂及物体的动力学方程,构建多机械臂系统模型,并将模糊算法与自适应滑模算法相结合;然后,对不确定因素及未知非线性项进行补偿,利用鲁棒算法对系统可靠性进行提升;最后,基于李雅普诺夫方法,证明了该系统的稳定性。仿真结果显示,该方法可显著提升控制器的控制精度和系统响应速度。

    Abstract:

    Subject to the friction and other uncertain factors, the multi-manipulator force & position hybrid controlling method is still a challenging issue. To investigate this problem, this paper proposes a hybrid control method based on fuzzy adaptive robust sliding mode algorithm. Firstly, the multi-manipulator system model is constructed with consideration of both multi-manipulator and the object dynamics equations. Secondly, the fuzzy algorithm is combined with the adaptive sliding mode algorithm to compensate for uncertain factors and unknown nonlinear terms, so as to improve the system reliability. Simulation results showed that, both controlling accuracy and responding time of the system can be improved by the proposed method.

    参考文献
    相似文献
    引证文献
引用本文

引文格式
朱志浩,李蔚,高直,等.基于模糊自适应滑模算法的多机械臂力/位混合控制 [J].集成技术,2023,12(3):72-81

Citing format
ZHU Zhihao, LI Wei, GAO Zhi, et al. Force/Position Hybrid Control of Multi-manipulator Based on Fuzzy Adaptive Sliding Mode Algorithm[J]. Journal of Integration Technology,2023,12(3):72-81

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:
  • 最后修改日期:
  • 录用日期:
  • 在线发布日期: 2023-05-11
  • 出版日期:
文章二维码