CPS: Medium: Collaborative Research:Virtual Sully: Autopilot with Multilevel Adaptation for Handling Large Uncertainties
CPS: Medium: Collaborative Research:Virtual Sully: Autopilot with Multilevel Adaptation for Handling Large Uncertainties
批准号:
1932288
负责人:
Evangelos Theodorou
金额:
$30.0万
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
项目状态:
已结题
起止时间:
2019-10-01 至 2024-02-29
中文摘要
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英文摘要
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.
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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
DOI:
--
发表时间:
2022
期刊:
IEEE International Conference on Robotics and Automation
影响因子:
--
作者:
[Yin, Ji, Zhang, Zhiyuan, Theodorou, Evangelos A., Tsiotras, Panagiotis]
通讯作者:
Tsiotras, Panagiotis
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:
Learning for Dynamics and Control
影响因子:
--
作者:
[Gahlawat, Aditya, Lakshmanan, Arun, Song, Lin, Patterson, Andrew, Wu, Zhuohuan, Hovakimyan, Naira, Theodorou, Evangelos A]
通讯作者:
Theodorou, Evangelos A
DOI:
--
发表时间:
2020
期刊:
影响因子:
--
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
DOI:
--
发表时间:
2021
期刊:
International Conference on Learning Representations
影响因子:
--
作者:
[Guan-Horng, Liu, Chen, Tianrong, Evangelos. A, Theodorou]
通讯作者:
Evangelos. A, Theodorou
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:
Physical review A General physics
影响因子:
--
作者:
[Evans, Ethan N., Wang, Ziyi, Frim, Adam G, DeWeese, Michael R., Theodorou, Evangelos A.]
通讯作者:
Theodorou, Evangelos A.
共 16 条
Collaborative Research: Real-Time Trajectory Generation Algorithms for Uncertain Autonomous Systems Based on Gaussian Processes
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批准号:1936079
-
项目类别:Standard Grant
-
资助金额:$23.67万
-
财政年份:2020
-
负责人:Evangelos Theodorou
-
依托单位:
I-Corps: Platform for Scaled Autonomous Vehicle Technology
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批准号:1747688
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项目类别:Standard Grant
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资助金额:$5.0万
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财政年份:2017
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负责人:Evangelos Theodorou
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依托单位:
Learning Optimal Control Using Forward Backward Stochastic Differential Equations
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批准号:1662523
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项目类别:Standard Grant
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资助金额:$34.95万
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财政年份:2017
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负责人:Evangelos Theodorou
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依托单位:
Workshop: Learning, Perception and Control in Robotics and Humans
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批准号:1542265
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项目类别:Standard Grant
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资助金额:$8.82万
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财政年份:2015
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负责人:Evangelos Theodorou
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依托单位:
海外基金