仮想センサーがローター故障時の空中浮遊を助ける(Virtual Sensors Help Aerial Vehicles Stay Aloft When Rotors Fail)

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2024-04-24 カリフォルニア工科大学(Caltech)

仮想センサーがローター故障時の空中浮遊を助ける(Virtual Sensors Help Aerial Vehicles Stay Aloft When Rotors Fail)
Chung’s group used a model of its Autonomous Flying Ambulance to test its NFFT control method.

カリフォルニア工科大学のチームが、機械学習を基にした新しい制御方法「Neural-Fly for Fault Tolerance」(NFFT)を開発しました。このシステムは、航空機が障害に遭遇した際に、それを検出し補正することができます。特に、追加のセンサーやハードウェアを必要とせず、飛行中の航空機の挙動を観察することで問題を検知します。この技術は、都市間の移動を目的としたハイブリッド電動飛行機に適用され、安全性と信頼性を高めることが期待されています。

<関連情報>

学習型ミニマムセンサ故障耐性適応飛行制御 Learning-Based Minimally-Sensed Fault-Tolerant Adaptive Flight Control

Michael O’Connell; Joshua Cho; Matthew Anderson;…
IEEE Robotics and Automation Letters  Published:16 April 2024
DOI:https://doi.org/10.1109/LRA.2024.3389414

Abstract

Many multirotor aircraft use redundant configurations to maintain control in the event of an actuator failure. Due to the redundancy of the system, fault isolation is inherently difficult and further compounded by complex interacting aerodynamics of the propellers, wings, and body. This letter presents a novel sparse failure identification and control correction method that does not require direct fault sensing, and instead utilizes only the vehicle’s dynamic response. The method couples an ℓ1 -regularized representation of the failure with a deep neural network to effectively isolate faults and improve tracking control in highly dynamic environments with unmodeled aerodynamic effects and unknown actuator failures. The method also includes a control re-allocation scheme which corrects for the identified faults while maximizing control authority and maintaining nominal performance characteristics. Experimental results demonstrate the method’s ability to maintain control of a multirotor aircraft by isolating motor failures and reallocating control, improving position tracking by 48 % over the baseline. This letter contributes to the development of robust fault detection and control strategies for over-actuated aircraft.

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