.png){kind=icon}
返回
Model-based update in task-level feedforward control using on-line approximation
.png){kind=icon}
PRE
AI
DOI：10.1016/S0005-1098(00)00178-3.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper proposes and studies an algorithm for task-level control based on a radial. basis function network approximation of the optimal task input vector on parameters of the task. A learning update scheme is proposed for on-line compensation for the inaccuracy of the model used in the controller design. The update approximates the Jacobian of the task input-output mapping using an off-line design model. Deadzone convergence of this learning scheme in the presence of modeling errors is proved and constructive estimates of the convergence robustness parameters are obtained. An application of the proposed algorithm to Feedforward vibration compensation for flexible spacecraft slewing complements the theoretical analysis. Simulations demonstrate practically acceptable performance of the algorithms in this difficult problem. (C) 2001 Elsevier Science Ltd. All rights reserved.
Keyword:
feedforward control
learning algorithm
convergence
neural network approximation
flexible spacecraft
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}
分享收藏
A convenient way of generating gamma random variables using generalized exponential distribution
PREAI
分享收藏 没有更多内容
没有更多内容