基座参数欠精确环境下双机械臂刚体夹持系统的自适应神经鲁棒控制

徐凡; 王进; 陆国栋

doi:10.1631/FITEE.1601707

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基座参数欠精确环境下双机械臂刚体夹持系统的自适应神经鲁棒控制

常规文章 | Updated：2022-05-19

- 基座参数欠精确环境下双机械臂刚体夹持系统的自适应神经鲁棒控制
- Adaptive robust neural control of a two-manipulator system holding a rigid object with inaccurate base frame parameters
- 信息与电子工程前沿（英文） 2018年19卷第11期页码：1316-1327
- Affiliations：
  
  State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China
- Author bio：
  
  Jin WANG, E-mail: dwjcom@zju.edu.cn
- Funds：
  
  Project supported by the National Natural Science Foundation of China (No. 51675470), the National Key R&D Program of China (No. 2017YFB1301203), and the Fundamental Research Funds for the Central Universities, China (No. 2017QNA4001)
- DOI：10.1631/FITEE.1601707
  中图分类号： TP241.2
- 收稿：2016-11-16，
  
  修回：2018-11-27，
  
  纸质出版：2018-11
- Accepted：
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徐凡, 王进, 陆国栋. 基座参数欠精确环境下双机械臂刚体夹持系统的自适应神经鲁棒控制[J]. 信息与电子工程前沿（英文）, 2018,19(11):1316-1327.

Fan XU, Jin WANG, Guo-dong LU. Adaptive robust neural control of a two-manipulator system holding a rigid object with inaccurate base frame parameters[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(11): 1316-1327.
徐凡, 王进, 陆国栋. 基座参数欠精确环境下双机械臂刚体夹持系统的自适应神经鲁棒控制[J]. 信息与电子工程前沿（英文）, 2018,19(11):1316-1327. DOI： 10.1631/FITEE.1601707.

Fan XU, Jin WANG, Guo-dong LU. Adaptive robust neural control of a two-manipulator system holding a rigid object with inaccurate base frame parameters[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(11): 1316-1327. DOI： 10.1631/FITEE.1601707.

摘要

针对基座参数欠精确环境下双机械臂刚体夹持系统的自适应调控问题进行研究。提出一种自适应神经鲁棒控制器，能同时解决基座参数欠精确、系统内力、建模不确定性、关节摩擦以及外部干扰等多种问题。该控制器采用一个径向基神经网络来逼近系统包括非预期内力在内的全部动力学部分。结合仿真实验和分析，该控制器能有效保证轨迹跟踪误差渐进收敛于0，并保持内力在可接受范围。在自适应调节机制下，该方法能对系统中双机械臂进一步在线精确标定。为保证系统全局稳定性，该控制器建立定制化鲁棒补偿，结合李雅普诺夫理论，证明该控制器在基座欠精确以及其他多种不确定环境下的鲁棒性。

Abstract

The problem of self-tuning control with a two-manipulator system holding a rigid object in the presence of inaccurate translational base frame parameters is addressed. An adaptive robust neural controller is proposed to cope with inaccurate translational base frame parameters

internal force

modeling uncertainties

joint friction

and external disturbances. A radial basis function neural network is adopted for all kinds of dynamical estimation

including undesired internal force. To validate the effectiveness of the proposed approach

together with simulation studies and analysis

the position tracking errors are shown to asymptotically converge to zero

and the internal force can be maintained in a steady range. Using an adaptive engine

this approach permits accurate online calibration of the relative translational base frame parameters of the involved manipulators. Specialized robust compensation is established for global stability. Using a Lyapunov approach

the controller is proved robust in the face of inaccurate base frame parameters and the aforementioned uncertainties.

关键词

Keywords

references

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