.png){kind=icon}
返回
Adaptive motion control using neural network approximations
.png){kind=icon}
PRE
AI
DOI：10.1016/S0005-1098(01)00192-3.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, we present a new adaptive technique for tracking control of mechanical systems in the presence of friction and periodic disturbances. Radial Basis Functions (RBFs) are used to compensate for the effects of nonlinearly occurring parameters in the friction and periodic disturbance model. Theoretical analysis, such as stability and transient performance, is provided. Furthermore, the performance of the adaptive RBF controller and its non-adaptive counterpart are compared. (C) 2001 Elsevier Science Ltd. All rights reserved.
Keyword:
adaptive control
compensation
friction
neural networks
uncertainty
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
5.9
论文数:
1.2W
被引数:
5.2W
机构
暂无机构信息
引用论文
.png){kind=icon}
Global well-posedness and scattering for the defocusing quintic NLS in three dimensions三维散焦五次NLS的全局适定性和散射
OAAI
OAAI分享收藏分享收藏Effects of Temperature and Humidity on Radio Signal Strength in Outdoor Wireless Sensor NetworksOAAI
OAAI分享收藏 没有更多内容
没有更多内容