引用本文:白 萍,方廷健,葛运建.基于计算转矩控制结构的机械手鲁棒神经网络补偿控制(英文)[J].控制理论与应用,2001,18(6):897~901.[点击复制]
BAI Ping,FANG Ting-jian,GE Yun-jian.Robust Neural-Network Compensating Control for Robot Manipulator Based on Computed Torque Control[J].Control Theory and Technology,2001,18(6):897~901.[点击复制]
基于计算转矩控制结构的机械手鲁棒神经网络补偿控制(英文)
Robust Neural-Network Compensating Control for Robot Manipulator Based on Computed Torque Control
摘要点击 1703  全文点击 1088  投稿时间：2000-07-17  修订日期：2001-02-14
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DOI编号  10.7641/j.issn.1000-8152.2001.6.018
  2001,18(6):897-901
中文关键词  机械手  计算转矩控制  神经网络  鲁棒  自适应
英文关键词  robot manipulator  computed torque control  neural network  robust  adaptive
基金项目  
作者单位
白 萍 中国科学技术大学 电子工程与信息科学系, 合肥 230026
中国科学院 合肥智能机械研究所,合肥 230031 
方廷健 中国科学院 合肥智能机械研究所, 合肥 230031 
葛运建 中国科学院 合肥智能机械研究所, 合肥 230031 
中文摘要
      提出了一种新的不确定性机器人跟踪控制策略. 文中基于计算转矩控制结构, 采用了函数链网络实现一个神经网络补偿器, 并叠加一个鲁棒控制项, 以补偿模型的不确定性部分. 另外, 还考虑了神经网络逼近误差非一致有界的情形, 设计了自适应的鲁棒控制项. 算法可保证跟踪误差及神经网络权估计最终一致有界. 与其它有关基于计算转矩控制的方法相比, 该算法既不需要测量关节角加速度, 也不要求惯性矩阵已知. 理论和仿真均证明了算法的可靠性和有效性.
英文摘要
      This paper proposes a new controller design approach for trajectory tracking of robot manipulator with uncertainties. The proposed controller is based on the computed torque control structure, and incorporates a compensator, which is realized by Functional Link Neural Network, and a robustifying term. In addition, when neural newtork reconstruction error is not uniformly bounded, an adaptive robustifying term is designed. It is shown that all the signals in the closed loop system are uniformly ultimately bounded. Compared with other approaches, no joint acceleration measurement and exactly known inertia matrix are required. Both theory and simulation results show the effectiveness of the proposed controller.