CPS: Medium: Collaborative Research:Virtual Sully: Autopilot with Multilevel Adaptation for Handling Large Uncertainties

CPS:中:协作研究:Virtual Sully:具有多级适应能力的自动驾驶仪,可处理较大的不确定性

基本信息

  • 批准号:
    1932288
  • 负责人:
  • 金额:
    $ 30万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2019
  • 资助国家:
    美国
  • 起止时间:
    2019-10-01 至 2024-02-29
  • 项目状态:
    已结题

项目摘要

During normal operations an aircraft is operated by its autopilot. When the autopilot sense a dangerous condition, near or outside of the flight envelope, the autopilot disengages itself, returning control to the pilot. Well-trained pilots typically can deal with modest out-of-envelope challenges. A pilot who can deal with a significantly compromised flight envelope is very remarkable as happened with Captain Chesley Sullenberger ("Sully") and co-pilot Jeffrey Skiles on US Airways Flight 1549 in 2009 when the aircraft struck a flock of geese just northeast of the George Washington Bridge and suddenly lost all engine power over Manhattan. The pilots glided their plane extraordinarily skillfully to a ditching in the Hudson River off Midtown Manhattan, saving all the passengers and averting a catastrophic crash in New York City. The National Transportation Safety Board official described it as the most successful ditching in aviation history. This capability to operate safely despite the exceptional situation well-outside the norm is the essence of this project, Virtual Sully.Virtual Sully technology is a development towards full pilotless autonomy, capable of identifying the failure/fault, estimating the remaining control authority, assessing the environment and planning a new feasible mission, doing path planning and executing it safely within the compromised flight envelope. This architecture replaces the traditional top-down one-way adaptation between mission planning, trajectory generation, tracking and stabilizing controller, with a two-way adaptation between mission planning, trajectory generation, and the adaptation of controller parameters to improve the stability and robustness of the control system. The following thrusts are considered: 1) monitoring and capability auditing; 2) high-assurance control with multi-level adaptation; 3) fault-tolerant architecture for unmanned autonomous systems (UAS) with real-time guarantees; 4) development of hardware-in-the loop simulation environment and flight tests using unmanned air vehicle (UAV) prototypes. Fault-tolerant computing infrastructure that can withstand high-stress situations will be integrated within flight control architecture that adapts at multiple levels. The feasibility evaluation of the missions and regenerated trajectories within UAV's remaining capabilities is pursued with real-time guarantees. The testbed is based on hardware-in-the-loop simulation for various failures, as well as extensive tests using real UAVs.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
在正常操作期间,飞机由其自动驾驶仪操作。当自动驾驶仪感应到接近或超出飞行包线的危险情况时,自动驾驶仪会自动脱离,将控制权交还给飞行员。训练有素的飞行员通常可以应对适度的挑战。一名能够应对严重受损的飞行包线的飞行员是非常了不起的,就像2009年美国航空公司1549航班的机长切斯利·萨伦伯格(“萨利”)和副驾驶杰弗里·斯科尔斯(Jeffrey Skiles)一样,当时飞机在乔治华盛顿大桥东北方向撞上了一群鹅,突然在曼哈顿上空失去了所有引擎动力。飞行员极其巧妙地将飞机滑行到曼哈顿中城附近的哈德逊河上的一个沟渠,拯救了所有乘客,避免了纽约市发生的一场灾难性坠机事故。 美国国家运输安全委员会官员称这是航空史上最成功的一次迫降。 这种在异常情况下安全运行的能力是虚拟萨利项目的精髓。虚拟萨利技术是一种完全无人自主的发展,能够识别故障/故障,估计剩余的控制权限,评估环境和规划新的可行使命,进行路径规划并在受损的飞行包线内安全执行。该体系结构取代了传统的自顶向下的使命规划、轨迹生成、跟踪和稳定控制器之间的单向自适应,采用了使命规划、轨迹生成和控制器参数自适应之间的双向自适应,提高了控制系统的稳定性和鲁棒性。考虑了以下重点:1)监控和能力审计; 2)具有多级自适应的高保证控制; 3)具有实时保证的无人驾驶自主系统(UAS)容错体系结构; 4)开发硬件在环仿真环境和使用无人驾驶飞行器(UAV)原型进行飞行测试。能够承受高压力情况的容错计算基础设施将被集成到适应多个级别的飞行控制架构中。在UAV剩余能力范围内的任务和再生轨迹的可行性评估是在实时保证下进行的。该试验台基于各种故障的硬件在环仿真,以及使用真实的无人机进行的广泛测试。该奖项反映了NSF的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。

项目成果

期刊论文数量(19)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Trajectory Distribution Control for Model Predictive Path Integral Control using Covariance Steering
使用协方差引导的模型预测路径积分控制的轨迹分布控制
Contraction L1-Adaptive Control using Gaussian Processes
使用高斯过程的收缩 L1 自适应控制
  • DOI:
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Gahlawat, Aditya;Lakshmanan, Arun;Song, Lin;Patterson, Andrew;Wu, Zhuohuan;Hovakimyan, Naira;Theodorou, Evangelos A
  • 通讯作者:
    Theodorou, Evangelos A
Feynman-Kac Neural Network Architectures for Stochastic Control Using Second-Order FBSDE Theory
  • DOI:
  • 发表时间:
    2020
  • 期刊:
  • 影响因子:
    0
  • 作者:
    M. Pereira;Ziyi Wang;T. Chen;Emily A. Reed;Evangelos A. Theodorou
  • 通讯作者:
    M. Pereira;Ziyi Wang;T. Chen;Emily A. Reed;Evangelos A. Theodorou
DDPNOpt: Differential Dynamic Programming Neural Optimizer
DDPNOpt:微分动态规划神经优化器
Learning quantum-state feedback control with backpropagation-free stochastic optimization
通过无反向传播随机优化学习量子态反馈控制
  • DOI:
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Evans, Ethan N.;Wang, Ziyi;Frim, Adam G;DeWeese, Michael R.;Theodorou, Evangelos A.
  • 通讯作者:
    Theodorou, Evangelos A.
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Evangelos Theodorou其他文献

100 Near-Infrared Spectroscopy as a surrogate for Vital Organ Perfusion during Cardiopulmonary Resuscitation in a Porcine Model of Cardiac Arrest
  • DOI:
    10.1016/s0300-9572(24)00414-3
  • 发表时间:
    2024-11-01
  • 期刊:
  • 影响因子:
  • 作者:
    Pierre Sebastian;Manan Gandhi;Luke Feeley;Alexander Oshin;Marinos Kosmopoulos;Anthony Prisco;Danielle Burroughs;Evangelos Theodorou;Demetris Yannopoulos
  • 通讯作者:
    Demetris Yannopoulos
emDe novo/em missense variants in phosphatidylinositol kinase PIP5KIγ underlie a neurodevelopmental syndrome associated with altered phosphoinositide signaling
磷脂酰肌醇激酶 PIP5KIγ 中的从头/错义变体是与磷酸肌醇信号改变相关的神经发育综合征的基础
  • DOI:
    10.1016/j.ajhg.2023.06.012
  • 发表时间:
    2023-08-03
  • 期刊:
  • 影响因子:
    8.100
  • 作者:
    Manuela Morleo;Rossella Venditti;Evangelos Theodorou;Lauren C. Briere;Marion Rosello;Alfonsina Tirozzi;Roberta Tammaro;Nour Al-Badri;Frances A. High;Jiahai Shi;Maria T. Acosta;Margaret Adam;David R. Adams;Raquel L. Alvarez;Justin Alvey;Laura Amendola;Ashley Andrews;Euan A. Ashley;Carlos A. Bacino;Guney Bademci;Brunella Franco
  • 通讯作者:
    Brunella Franco
P114 MCH LINKS IMMUNOMETABOLSIM TO INTESTINAL INFLAMMATION
  • DOI:
    10.1053/j.gastro.2019.01.180
  • 发表时间:
    2019-02-01
  • 期刊:
  • 影响因子:
  • 作者:
    Evangelos Theodorou;Kenneth Swanson;Alan Moss;Efi Kokkotou
  • 通讯作者:
    Efi Kokkotou
Forecast accuracy and inventory performance: Insights on their relationship from the M5 competition data
预测准确性与库存绩效:从 M5 竞赛数据中对它们之间关系的见解
  • DOI:
    10.1016/j.ejor.2024.12.033
  • 发表时间:
    2025-04-16
  • 期刊:
  • 影响因子:
    6.000
  • 作者:
    Evangelos Theodorou;Evangelos Spiliotis;Vassilios Assimakopoulos
  • 通讯作者:
    Vassilios Assimakopoulos
328 – Preclinical Drug Evaluation Reveals Inhibition of Nacylethanolamine Acid Amidase As a Potential Treatment for Inflammatory Bowel Disease
  • DOI:
    10.1016/s0016-5085(19)36948-3
  • 发表时间:
    2019-05-01
  • 期刊:
  • 影响因子:
  • 作者:
    Evangelos Theodorou;Shrouq Farah;Katharine A. Germansky;Jonathan Glickman;Alexandros Makriyannis;Alan C. Moss;Michael S. Malamas;Efi Kokkotou
  • 通讯作者:
    Efi Kokkotou

Evangelos Theodorou的其他文献

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{{ truncateString('Evangelos Theodorou', 18)}}的其他基金

Collaborative Research: Real-Time Trajectory Generation Algorithms for Uncertain Autonomous Systems Based on Gaussian Processes
合作研究:基于高斯过程的不确定自治系统实时轨迹生成算法
  • 批准号:
    1936079
  • 财政年份:
    2020
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant
I-Corps: Platform for Scaled Autonomous Vehicle Technology
I-Corps:大规模自动驾驶汽车技术平台
  • 批准号:
    1747688
  • 财政年份:
    2017
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant
Learning Optimal Control Using Forward Backward Stochastic Differential Equations
使用前向后向随机微分方程学习最优控制
  • 批准号:
    1662523
  • 财政年份:
    2017
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant
Workshop: Learning, Perception and Control in Robotics and Humans
研讨会:机器人和人类的学习、感知和控制
  • 批准号:
    1542265
  • 财政年份:
    2015
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant

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