Robotics & Machine Learning Daily News2024,Issue(Jun.5) :89-89.

Studies from California Institute of Technology (Caltech) Add New Findings in th e Area of Robotics and Automation (Learning-based Minimally-sensed Fault-toleran t Adaptive Flight Control)

加州理工学院(Caltech)的研究在机器人和自动化(基于学习的最小感知容错和自适应飞行控制)领域增加了新的发现

Robotics & Machine Learning Daily News2024,Issue(Jun.5) :89-89.

Studies from California Institute of Technology (Caltech) Add New Findings in th e Area of Robotics and Automation (Learning-based Minimally-sensed Fault-toleran t Adaptive Flight Control)

加州理工学院(Caltech)的研究在机器人和自动化(基于学习的最小感知容错和自适应飞行控制)领域增加了新的发现

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摘要

机器人与机器学习每日新闻-机器人与自动化的最新研究结果已经发表。根据NewsRx记者从加州帕萨迪纳发回的消息,研究表明:“许多多旋翼飞机使用冗余配置来在ACTUAR发生故障时保持控制。由于系统的冗余性,故障隔离本质上很困难,并且由于PR桨、机翼和机身复杂的相互作用的空气动力学而变得更加复杂。”这项研究的财政支持来自Supernal,LLC。我们的新闻记者从加州理工学院(Caltech)的研究中获得了一句话,"这封信提出了一种新的稀疏故障识别和控制校正方法,不需要直接故障检测。该方法将故障的l(1)-regularized表示与深度神经网络相结合,有效地隔离故障,改善了在高动态环境下的跟踪控制,具有未建模气动力效应和未知执行器故障,同时对故障进行校正,同时最大限度地提高了控制权限和保持标称性能。实验结果表明,该方法能够通过隔离电机故障和重新分配控制来保持多旋翼飞机的控制,使位置跟踪比基线提高48%。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Robotics - Ro botics and Automation have been published. According to news originating from Pa sadena, California, by NewsRx correspondents, research stated, “Many multirotor aircraft use redundant configurations to maintain control in the event of an act uator failure. Due to the redundancy of the system, fault isolation is inherentl y difficult and further compounded by complex interacting aerodynamics of the pr opellers, wings, and body.” Financial support for this research came from Supernal, LLC. Our news journalists obtained a quote from the research from the California Inst itute of Technology (Caltech), “This letter presents a novel sparse failure iden tification and control correction method that does not require direct fault sens ing, and instead utilizes only the vehicle’s dynamic response. The method couple s an l(1)-regularized representation of the failure with a deep neural network t o effectively isolate faults and improve tracking control in highly dynamic envi ronments with unmodeled aerodynamic effects and unknown actuator failures. The m ethod also includes a control re-allocation scheme which corrects for the identi fied faults while maximizing control authority and maintaining nominal performan ce characteristics. Experimental results demonstrate the method’s ability to mai ntain control of a multirotor aircraft by isolating motor failures and reallocat ing control, improving position tracking by 48 % over the baseline .”

Key words

Pasadena/California/United States/Nor th and Central America/Robotics and Automation/Robotics/California Institute of Technology (Caltech)

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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