旋翼机状态识别的运动原语视角

Umberto Saetti, Jonathan D. Rogers
{"title":"旋翼机状态识别的运动原语视角","authors":"Umberto Saetti, Jonathan D. Rogers","doi":"10.4050/f-0076-2020-16266","DOIUrl":null,"url":null,"abstract":"\n An alternative approach to regime recognition that is based on the notion of motion primitives is developed. The algorithm developed is non-causal and leverages the ideas of maneuvers and trims as defined in a motion primitive context. The algorithm functions in three major steps. Given a state and control input time history obtained from flight data, the first step consists of classifying the state and control time history into trim and maneuver segments. The second step leverages the information in the trim state and control vectors to classify each trim segment into a particular trim condition based on conditional (if-else-if) logic. The third step entails the classification of each maneuver segment (flown between two trim segments) as a particular maneuver condition. Importantly, maneuver classification leverages dynamic time warping in order to compensate for rate and time duration variations. Accuracy of the proposed algorithm is evaluated using SH-60B simulated flight data. Operation of the algorithm is also demonstrated using real-world piloted flight test data from a generic utility helicopter.\n","PeriodicalId":293921,"journal":{"name":"Proceedings of the Vertical Flight Society 76th Annual Forum","volume":"333 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Motion Primitive Perspective on Rotorcraft Regime Recognition\",\"authors\":\"Umberto Saetti, Jonathan D. Rogers\",\"doi\":\"10.4050/f-0076-2020-16266\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n An alternative approach to regime recognition that is based on the notion of motion primitives is developed. The algorithm developed is non-causal and leverages the ideas of maneuvers and trims as defined in a motion primitive context. The algorithm functions in three major steps. Given a state and control input time history obtained from flight data, the first step consists of classifying the state and control time history into trim and maneuver segments. The second step leverages the information in the trim state and control vectors to classify each trim segment into a particular trim condition based on conditional (if-else-if) logic. The third step entails the classification of each maneuver segment (flown between two trim segments) as a particular maneuver condition. Importantly, maneuver classification leverages dynamic time warping in order to compensate for rate and time duration variations. Accuracy of the proposed algorithm is evaluated using SH-60B simulated flight data. Operation of the algorithm is also demonstrated using real-world piloted flight test data from a generic utility helicopter.\\n\",\"PeriodicalId\":293921,\"journal\":{\"name\":\"Proceedings of the Vertical Flight Society 76th Annual Forum\",\"volume\":\"333 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Vertical Flight Society 76th Annual Forum\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4050/f-0076-2020-16266\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Vertical Flight Society 76th Annual Forum","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4050/f-0076-2020-16266","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

提出了一种基于运动原语概念的状态识别方法。所开发的算法是非因果的,并且利用了在运动原语上下文中定义的机动和修剪的思想。该算法分为三个主要步骤。给定从飞行数据中获得的状态和控制输入时间历史,第一步是将状态和控制时间历史分为微调段和机动段。第二步利用修剪状态和控制向量中的信息,根据条件(if-else-if)逻辑将每个修剪段分类为特定的修剪条件。第三步需要将每个机动段(在两个纵段之间飞行)分类为特定的机动条件。重要的是,机动分类利用动态时间规整来补偿速率和时间持续时间的变化。利用SH-60B模拟飞行数据对算法的精度进行了评价。该算法的操作还通过通用通用直升机的实际驾驶飞行测试数据进行了演示。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Motion Primitive Perspective on Rotorcraft Regime Recognition
An alternative approach to regime recognition that is based on the notion of motion primitives is developed. The algorithm developed is non-causal and leverages the ideas of maneuvers and trims as defined in a motion primitive context. The algorithm functions in three major steps. Given a state and control input time history obtained from flight data, the first step consists of classifying the state and control time history into trim and maneuver segments. The second step leverages the information in the trim state and control vectors to classify each trim segment into a particular trim condition based on conditional (if-else-if) logic. The third step entails the classification of each maneuver segment (flown between two trim segments) as a particular maneuver condition. Importantly, maneuver classification leverages dynamic time warping in order to compensate for rate and time duration variations. Accuracy of the proposed algorithm is evaluated using SH-60B simulated flight data. Operation of the algorithm is also demonstrated using real-world piloted flight test data from a generic utility helicopter.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信