控制理论与应用2001，Vol.18Issue(6)：897-901,5.
基于计算转矩控制结构的机械手鲁棒神经网络补偿控制
Robust Neural-Network Compensating Control for Robot Manipulator Based on Computed Torque Control
白萍 1方廷健 2葛运建3
作者信息
- 1. 中国科学院合肥智能机械研究所
- 2. 中国科学技术大学电子工程与信息科学系
- 3. 中国科学院合肥智能机械研究所,
- 折叠
摘要
Abstract
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.关键词
机械手/计算转矩控制/神经网络/鲁棒/自适应分类
信息技术与安全科学引用本文复制引用
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[J].控制理论与应用,2001,18(6):897-901,5.
