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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：
1932529
负责人：
Lui Sha
金额：
$ 90万
依托单位：
University of Illinois at Urbana-Champaign
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 2023-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1932529&HistoricalAwards=false
关键词：
CPS Medium Collaborative Research Virtual

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.

正常飞行时，飞机由自动驾驶仪操作。当自动驾驶仪感知到危险情况时，在飞行包线附近或外，自动驾驶仪就会脱离，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极限挑战。能够处理严重受损的飞行包线的飞行员是非常出色的，就像2009年全美航空公司1549航班机长切斯利·萨伦伯格（“萨利”）和副驾驶杰弗里·斯基尔斯所发生的那样，当时飞机在乔治·华盛顿大桥东北方向撞上了一群鹅，在曼哈顿上空突然失去了所有引擎动力。飞行员非常熟练地将飞机滑到曼哈顿中城外哈德逊河的一个沟渠上，拯救了所有乘客，避免了在纽约市发生灾难性的坠机事故。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这个项目的本质，虚拟萨利。虚拟污迹技术是向完全无人驾驶自主发展的一种技术，能够识别故障/故障，估计剩余的控制权限，评估环境并规划新的可行任务，在受损的飞行包线内进行路径规划并安全执行。该体系结构取代了传统自上而下的任务规划、轨迹生成、跟踪和稳定控制器之间的单向自适应，改为任务规划、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。考虑以下重点：1)监视和能力审计；2)多级自适应的高保证控制；3)具有实时性保证的无人自主系统（UAS）容错架构；4)开发硬件在环仿真环境和使用无人机原型机进行飞行测试。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体系结构中。在UAV的剩余能力范围内对任务和再生轨迹的可行性评估进行实时保证。该测试平台基于各种故障的硬件在环模拟，以及使用真实无人机进行的广泛测试。该奖项反映了美国国家科学基金会的法定使命，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（16）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Robust Vehicle Lane Keeping Control with Networked Proactive Adaptation

具有网络主动适应功能的强大车辆车道保持控制

DOI：
10.23919/acc50511.2021.9482669
发表时间：
2021
期刊：
American Control Conference
影响因子：
0
作者：
Kim, Hunmin;Wan, Wenbin;Hovakimyan, Naira;Sha, Lui;Voulgaris, Petros
通讯作者：
Voulgaris, Petros

Contraction L1-Adaptive Control using Gaussian Processes. In Learning for Dynamics and Control

使用高斯过程的收缩 L1 自适应控制。

DOI：
发表时间：
2021
期刊：
Proceedings of Machine Learning Research
影响因子：
0
作者：
Gahlawat, A.
通讯作者：
Gahlawat, A.

RRT Guided Model Predictive Path Integral Method

DOI：
10.23919/acc55779.2023.10155837
发表时间：
2023-01
期刊：
2023 American Control Conference (ACC)
影响因子：
0
作者：
Chuyuan Tao;Hunmin Kim;N. Hovakimyan
通讯作者：
Chuyuan Tao;Hunmin Kim;N. Hovakimyan

L1 adaptive control with Bayesian learning. In Learning for Dynamics and Control

使用贝叶斯学习的 L1 自适应控制。

DOI：
发表时间：
2020
期刊：
Machine learning research
影响因子：
0
作者：
Gahlawat, A.
通讯作者：
Gahlawat, A.

Adaptive Risk Sensitive Path Integral for Model Predictive Control via Reinforcement Learning

通过强化学习进行模型预测控制的自适应风险敏感路径积分

DOI：
10.1109/med59994.2023.10185876
发表时间：
2023
期刊：
editerranean Conference on Control and Automation (MED
影响因子：
0
作者：
Yoon, Hyung-Jin;Tao, Chuyuan;Kim, Hunmin;Hovakimyan, Naira;Voulgaris, Petros
通讯作者：
Voulgaris, Petros