.png){kind=icon}
返回
Microsatellite Uncertainty Control Using Deterministic Artificial Intelligence
.png){kind=icon}
OA
AI
DOI：10.3390/s22228723.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This manuscript explores the applications of deterministic artificial intelligence (DAI) in a space environment in response to unknown sensor noise and sudden changes in craft physical parameters. The current state of the art literature has proposed the method, but only ideal environments, and accordingly this article addresses the literature gaps by critically evaluating efficacy in the face of unaddressed parametric uncertainties. We compare an idealized combined non-linear feedforward (FFD) and linearized feedback (FB) control scheme with an altered feedforward, feedback, and deterministic artificial intelligence scheme in the presence of simulated craft damage and environmental disturbances. Mean trajectory tracking error was improved over 91%, while the standard deviation was improved over 97% whilst improving (reducing) control effort by 13%.
Keyword:
control systems
feedforward
feedback
adaptive control
deterministic artificial intelligence
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:
3.5
论文数:
7.2W
被引数:
20.9W
机构
引用论文
Autonomous Trajectory Generation Comparison for De-Orbiting with Multiple Collision Avoidance.png)
SENSORSOAAI
SENSORSIF3.5
OAAI分享收藏Convergence to harmonic maps for the Landau-Lifshitz flows between two dimensional hyperbolic spaces
OAAI
OAAI分享收藏
查看更多内容