中国机械工程

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基于递归小脑神经网络模型控制的空间机器人关节抗死区及摩擦控制

黄小琴1,2;陈力1,2   

  1. 1.福州大学机械工程及自动化学院,福州,350116
    2.福州大学福建省高端装备制造协同创新中心,福州,350116
  • 出版日期:2018-01-25 发布日期:2018-01-22
  • 基金资助:
    国家自然科学基金资助项目(11372073,11072061);
    福建省工业机器人基础部件技术重大研发平台项目(2014H21010011)
    National Natural Science Foundation of China (No. 11372073,11072061)

Anti-dead-zones and Friction Control of Space Robots with Recurrent CMAC

HUANG Xiaoqin1,2;CHEN Li1,2   

  1. 1.School of Mechanical Engineering and Automation,Fuzhou University,Fuzhou,350116
    2.Collaborative Innovation Center of High End Equipment Manufacturing in Fujian,Fuzhou University,Fuzhou,350116
  • Online:2018-01-25 Published:2018-01-22
  • Supported by:
    National Natural Science Foundation of China (No. 11372073,11072061)

摘要: 探讨了存在关节力矩输出死区、摩擦与外部干扰的载体位姿均不受控的漂浮基空间机器人系统的动力学控制问题。设计了一种递归小脑模型关节控制器(CMAC)神经网络与死区估计补偿器,使两关节铰能够跟踪期望运动轨迹。该控制器利用摩擦双观测器估计不可测的内部摩擦状态,利用死区预补偿器消除关节力矩输出死区的影响;应用递归小脑神经网络模型逼近了包含摩擦误差及外部干扰的动力学方程不确定项。仿真结果表明了该控制方法的有效性。

关键词: 空间机器人, 死区, LuGre摩擦, 递归小脑神经网络模型控制

Abstract: Dynamics control of free-floating space robot systems with joint torque output dead-zones, friction and external disturbances were discussed. A recurrent CMAC and an adaptive dead-zone compensator were designed to track the desired trajectories of two joints. Friction double observers were designed to estimate the unmeasurable internal friction states, and dead-zone precompensator was designed to eliminate the impacts of joint torque output dead-zones. The recurrent cerebellar neural network model was applied to approximate uncertainty terms of dynamics equation including friction errors and external disturbances. The results of simulation show the efficiency of the proposed control method.

Key words: space robot, dead zone, LuGre friction, cerebellar model articulation controller(CMAC)

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