CAREER: Perceivability: Enabling Safe and Secure Autonomy via Synergistic Control, Observation and Learning

职业：可感知性：通过协同控制、观察和学习实现安全可靠的自治

• 批准号: 1942907
• 负责人: Dimitra Panagou
• 金额: $ 58.4万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1942907&HistoricalAwards=false
• 关键词: CAREER; Perceivability; Enabling; Safe; Secure

This Faculty Early Career Development (CAREER) grant will address fundamental questions in control and estimation theory by establishing the concept of perceivability: the structural property of a system that describes the ability to build knowledge about an environment dynamically, in the face of constraints. Autonomous systems (e.g., drones, self-driving cars) must be able to safely and timely learn the environment they operate in, to enable them to interact safely with humans and each other. This process depends on the structure (e.g., dynamics, constraints) and goals of the system, and information that is unreliable due to sensor faults, or untrusted due to malicious actions. The fundamental perceivability question is: “Is it feasible to safely learn a given environment for given dynamics, sensing and communication capabilities, under a given learning algorithm, within a given time horizon?” If the answer is negative, then one may wonder: “What parameters of the system can be changed such that the environment can be learned? What are the synergies between control and observation that safely enhance the generation of knowledge?” The project will develop the foundations of perceivability, and computationally-efficient learning and control techniques towards increasing system safety, autonomy and resilience. It will be complemented by an educational and outreach program that will engage underrepresented groups and K-12 students and disseminate the results via outreach activities and institutional STEM programs. Perceivability introduces a game-changing concept in systems science that aims to bridge the gap between learning, estimation and control, and enables new capabilities in systems engineering. An environment is called perceivable within some time horizon if there exists a safe control input, and therefore a safe trajectory of the physical system, that enables the collection of data over which the environment can be learned. Perceivability can thus be thought of as a generalized property of an intelligent system: a merging of reachability and observability that tightly links the knowledge-building process with the system dynamics and constraints. The project will investigate how the system structure and the underlying control, estimation and learning mechanisms (i) enable the ability to characterize whether an environment is perceivable within a given time horizon, over safe system trajectories while using uncertain (i.e., faulty or malicious) information, and (ii) how the system structure and/or the knowledge-building mechanism can be altered to achieve safe knowledge generation. The innovations will enable autonomous systems to accomplish intelligent, complex tasks in safety-critical and time-critical situations.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.

这项教师早期职业发展（职业）赠款将通过建立可感知性的概念来解决控制和估计理论中的基本问题：一个系统的结构性属性，该系统描述了面对约束的能力，该系统描述了动态建立有关环境知识的能力。自主系统（例如，无人机，自动驾驶汽车）必须能够安全，及时地学习其操作的环境，以使它们能够与人类和彼此的互动。此过程取决于系统的结构（例如，动态，约束）和系统的目标，以及由于传感器故障而导致的信息，或由于恶意行动而受到不可靠的信息。基本的可感知性问题是：“在给定的学习算法下，在给定的学习算法下，安全地学习给定的动态，灵敏度和交流能力的给定环境是否可行？”如果答案是负面的，那么人们可能会想知道：“可以更改系统的哪些参数，以便可以学习环境？控制和观察之间可以安全地增强知识的产生？”该项目将开发可感知性的基础，以及计算高效的学习和控制技术，以提高系统安全性，自主权和弹性。它将由一项教育和推广计划完成，该计划将与代表性不足的群体和K-12学生互动，并通过外展活动和机构STEM计划传播结果。可感知性引入了系统科学中改变游戏规则的概念，旨在弥合学习，估计和控制之间的差距，并在系统工程中实现新的功能。如果存在安全的控制输入，并且可以在某个时间范围内感知环境，从而使环境可以安全地轨迹，从而可以收集可以学习环境的数据。因此，可以将可感知性视为智能系统的广义特性：意识和观察的合并，将知识构建过程与系统动态和约束紧密联系在一起。该项目将调查系统结构和基础控制，估计和学习机制（i）如何使能够表征在给定时间范围内是否可以感知环境，而在安全的系统轨迹上可以感知到一个不确定的（即错误或恶意）信息，以及（ii）系统结构和/或知识机制如何改变知识的知识。这些创新将使自主系统能够在安全至关重要和关键时期情况下完成智力，复杂的任务。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响评估标准，被认为是通过评估来获得的支持。

项目成果

A Constructive Method for Designing Safe Multirate Controllers for Differentially-Flat Systems

• DOI: 10.1109/lcsys.2021.3136465
• 发表时间: 2024-03
• 期刊: IEEE Control Systems Letters
• 影响因子: 3
• 作者: Devansh R. Agrawal;Hardik Parwana;Ryan K. Cosner;Ugo Rosolia;A. Ames;Dimitra Panagou

Multi-Rate Control Design Under Input Constraints via Fixed-Time Barrier Functions

• DOI: 10.1109/lcsys.2021.3084322
• 发表时间: 2021-03
• 期刊: IEEE Control Systems Letters
• 影响因子: 3
• 作者: Kunal Garg;Ryan K. Cosner;Ugo Rosolia;A. Ames;Dimitra Panagou

Control Barrier Functions in Sampled-Data Systems

控制采样数据系统中的势垒函数

• DOI: 10.1109/lcsys.2021.3076127
• 发表时间: 2022
• 期刊: IEEE Control Systems Letters
• 影响因子: 3
• 作者: Breeden, Joseph;Garg, Kunal;Panagou, Dimitra
• 通讯作者: Panagou, Dimitra

Safe Control Synthesis via Input Constrained Control Barrier Functions

通过输入约束控制屏障函数进行安全控制综合

• DOI: 10.1109/cdc45484.2021.9682938
• 发表时间: 2021
• 期刊: 2021 60th Conference on Decision and Control
• 作者: Agrawal, Devansh R.;Panagou, Dimitra
• 通讯作者: Panagou, Dimitra

Safe and Robust Observer-Controller Synthesis Using Control Barrier Functions

使用控制屏障函数的安全且鲁棒的观察者控制器综合

• DOI: 10.1109/lcsys.2022.3185142
• 发表时间: 2023
• 期刊: IEEE Control Systems Letters
• 影响因子: 3
• 作者: Agrawal, Devansh R.;Panagou, Dimitra
• 通讯作者: Panagou, Dimitra