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CPS: Small: Self-Improving Cyber-Physical Systems

CPS：小型：自我改进的网络物理系统

基本信息

批准号：
1740079
负责人：
Susmit Jha
金额：
$ 49.98万
依托单位：
SRI International
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-10-01 至 2022-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1740079&HistoricalAwards=false
关键词：
CPS Small Self Improving Cyber

项目摘要

Traditional cyber-physical systems operate in heavily constrained and controlled environments with limited exposure to unexpected changes and uncertainties. Examples include robots operating on manufacturing assembling-lines and cyber-physical control systems of chemical plants. The model-based design paradigm, where design, implementation and verification are all guided by mathematical models of the system, has proven to be very successful in building such non-adaptive cyberphysical systems and proving their safety. The recent success of data-driven approaches based on the collection of a large amount of data followed by learning and inference has enabled modern cyberphysical systems to be more adaptive. Examples include self-driving cars and warehouse robots. Learning algorithms embedded in these systems allow them to learn as they execute and modify their behavior as needed. Such systems are capable of a wide range of non-preprogrammed behaviors. But this creates a new challenge. Model-based design paradigm is no longer sufficient. Formal guarantees on safety, robustness or improvement in performance are difficult to establish since the system evolution is no longer static; instead, it is data-driven and guided by the system's dynamic experience. The goal of this project is to build and evaluate a formal framework that combines data-driven and model-based development of adaptive cyber-physical systems.This project develops a new approach for designing safe, data-driven, and model-based adaptive cyber-physical systems (CPS). Model-based techniques are used initially to bootstrap the system and find the most liberal safety envelope for the system. A combination of design robustness and runtime monitoring of quantitatively-interpreted rich temporal logic is used to keep the system within the safety envelope. Data-driven techniques are used to actively explore, adapt, and improve system performance while constraining the system behavior to lie within the safety envelope. New data is summarized by tight learning of temporal logic properties from it; the learned logical specification is, in turn, used to guide active exploration. The key advances in this project include (a) data as model paradigm, where data from past runs is treated as a first-class object in the design of CPS, (b) tight learning from positive-only examples, where previous runs (that are all safe runs, and hence provide only positive examples) are summarized into rich temporal logic formulae, (c) safety envelope synthesis for robustness-metric guided monitoring and optimization of system performance within the envelope, (d) data-driven extensions of model-based control, where data is used to extend classical model-predictive control, and (e) active exploration, where an adaptive CPS actively executes some safe manoeuvres solely for the purpose of improving its knowledge and performance.

传统的网络物理系统在受到严格限制和控制的环境中运行，受到意外变化和不确定性的影响有限。例子包括在制造装配线上运行的机器人和化工厂的网络物理控制系统。基于模型的设计范式，其中设计、实现和验证都由系统的数学模型指导，已被证明在构建此类非自适应网络物理系统并证明其安全性方面非常成功。最近基于收集大量数据并进行学习和推理的数据驱动方法取得了成功，使现代网络物理系统更具适应性。例子包括自动驾驶汽车和仓库机器人。这些系统中嵌入的学习算法使它们能够在执行时学习并根据需要修改其行为。此类系统能够执行多种非预编程行为。但这带来了新的挑战。基于模型的设计范式不再足够。由于系统演进不再是静态的，因此很难建立对安全性、稳健性或性能改进的正式保证；相反，它是数据驱动的，并由系统的动态体验引导。该项目的目标是构建和评估一个正式框架，该框架将数据驱动和基于模型的自适应网络物理系统开发结合起来。该项目开发了一种设计安全、数据驱动和基于模型的自适应网络物理系统（CPS）的新方法。基于模型的技术最初用于引导系统并找到系统最自由的安全范围。设计稳健性和定量解释的丰富时序逻辑的运行时监控相结合，用于将系统保持在安全范围内。数据驱动技术用于主动探索、适应和提高系统性能，同时将系统行为限制在安全范围内。通过严格学习时态逻辑属性来总结新数据；学到的逻辑规范反过来又用于指导主动探索。该项目的关键进展包括（a）数据作为模型范式，其中来自过去运行的数据被视为 CPS 设计中的一流对象，（b）从仅正面示例中进行严格学习，其中以前的运行（都是安全运行，因此仅提供正面示例）被总结为丰富的时序逻辑公式，（c）安全包络综合，用于鲁棒性度量引导监控和系统性能优化包络，（d）基于模型的控制的数据驱动扩展，其中数据用于扩展经典模型预测控制，以及（e）主动探索，其中自适应 CPS 主动执行一些安全操作，其目的只是为了提高其知识和性能。