CPS: Medium: Collaborative Research: Learning and Verifying Conformant Data-Driven Models for Cyber-Physical Systems

CPS：媒介：协作研究：学习和验证网络物理系统的一致数据驱动模型

• 批准号: 1932189
• 负责人: Sriram Sankaranarayanan
• 金额: $ 59.22万
• 依托单位: University of Colorado at Boulder
• 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 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1932189&HistoricalAwards=false
• 关键词: CPS; Medium; Collaborative; Research; Learning

This project investigates fundamental techniques for building mathematical models that can be safely used to make trustworthy predictions and control decisions. Mathematical models form the foundation for modern Cyber-Physical Systems (CPS). Examples include vehicle models that predict how a car will move when brakes are applied, or physiological models that predict how the blood glucose levels change in a patient with type-1 diabetes when insulin is administered. The success of machine learning tools has yielded data-driven models such as neural networks. However, depending on how data is collected and the models are learned, it is possible to obtain models that violate fundamental physical, chemical, or physiological facts that can potentially threaten life and property. The approach of the project is to expose these model flaws through advanced analysis. The project seeks to broaden participation in computing through mentoring activities that will encourage undergraduate women and members of underrepresented minority groups to consider a career in research.The research combines falsification methods for exposing failure to conform with verification approaches for rigorously proving conformance. Furthermore, approaches for learning models of dynamical systems from data and imposing core cyber-physical domain knowledge are under investigation. The project is applying these data-driven models with conformance guarantees to the design of safe controllers for autonomous vehicles, models of human insulin glucose regulation and robotic swarms. The effort is advancing CPS education by creating a framework for distance education focused on CPS. The researchers are developing a series of low cost hardware testbeds and self-paced learning tasks that will expose students to the process of building highly reliable and safety critical CPS.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.

该项目研究了建立数学模型的基本技术，这些模型可以安全地用于做出可靠的预测和控制决策。数学模型是现代信息物理系统（CPS）的基础。例如，车辆模型可以预测汽车在刹车时如何移动，生理模型可以预测1型糖尿病患者在注射胰岛素时血糖水平如何变化。机器学习工具的成功产生了数据驱动的模型，如神经网络。然而，根据数据收集和模型学习的方式，有可能获得违反可能威胁生命和财产的基本物理，化学或生理事实的模型。该项目的方法是通过高级分析暴露这些模型缺陷。该项目旨在通过指导活动来扩大对计算的参与，鼓励本科女生和代表性不足的少数群体考虑从事研究工作。该研究将揭露不符合的伪造方法与严格证明符合性的验证方法相结合。此外，正在研究从数据中学习动力系统模型和强加核心网络物理领域知识的方法。该项目将这些具有一致性保证的数据驱动模型应用于自动驾驶汽车的安全控制器设计，人类胰岛素葡萄糖调节模型和机器人群。这项工作正在通过创建一个以CPS为重点的远程教育框架来推进CPS教育。研究人员正在开发一系列低成本的硬件测试平台和自定进度的学习任务，这些任务将使学生接触到构建高度可靠和安全关键CPS的过程。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

项目成果

Safe Robot Learning in Assistive Devices through Neural Network Repair

• DOI: 10.48550/arxiv.2303.04431
• 发表时间: 2023-03
• 作者: K. Majd;Geoffrey Clark;Tanmay Khandait;Siyu Zhou;S. Sankaranarayanan;Georgios Fainekos;H. B. Amor

Counterexample-guided computation of polyhedral Lyapunov functions for piecewise linear systems

• DOI: 10.1016/j.automatica.2023.111165
• 发表时间: 2023-09
• 期刊: Autom.
• 作者: Guillaume O. Berger;S. Sankaranarayanan

Training Neural Network Controllers Using Control Barrier Functions in the Presence of Disturbances

• DOI: 10.1109/itsc45102.2020.9294485
• 发表时间: 2020-01
• 期刊: 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)
• 作者: Shakiba Yaghoubi;Georgios Fainekos;S. Sankaranarayanan

An Algorithm for Learning Switched Linear Dynamics from Data

一种从数据中学习切换线性动力学的算法

• 发表时间: 2022
• 期刊: Neural Information Processing Systems (NeurIPS’22
• 作者: Berger, Guillaume O.;Narasimhamurthy, Monal;Watanabe, Kandai;Lahijanian, Morteza;Sankaranarayanan, Sriram
• 通讯作者: Sankaranarayanan, Sriram

Decoding Output Sequences for Discrete-Time Linear Hybrid Systems.

解码离散时间线性混合系统的输出序列。

• DOI: 10.1145/3501710.3519530
• 发表时间: 2022
• 期刊: 25th ACM International Conference on Hybrid Systems: Computation and Control
• 作者: Narasimhamurthy, Monal;Sankaranarayanan, Sriram
• 通讯作者: Sankaranarayanan, Sriram