CPS: TTP Option: Frontiers: Collaborative Research: Software Defined Control for Smart Manufacturing Systems

CPS：TTP 选项：前沿：协作研究：智能制造系统的软件定义控制

• 批准号: 1544678
• 负责人: Kira Barton
• 金额: $ 236.24万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1544678&HistoricalAwards=false
• 关键词: CPS; TTP; Option; Frontiers; Collaborative

Software-Defined Control (SDC) is a revolutionary methodology for controlling manufacturing systems that uses a global view of the entire manufacturing system, including all of the physical components (machines, robots, and parts to be processed) as well as the cyber components (logic controllers, RFID readers, and networks). As manufacturing systems become more complex and more connected, they become more susceptible to small faults that could cascade into major failures or even cyber-attacks that enter the plant, such as, through the internet. In this project, models of both the cyber and physical components will be used to predict the expected behavior of the manufacturing system. Since the components of the manufacturing system are tightly coupled in both time and space, such a temporal-physical coupling, together with high-fidelity models of the system, allows any fault or attack that changes the behavior of the system to be detected and classified. Once detected and identified, the system will compute new routes for the physical parts through the plant, thus avoiding the affected locations. These new routes will be directly downloaded to the low-level controllers that communicate with the machines and robots, and will keep production operating (albeit at a reduced level), even in the face of an otherwise catastrophic fault. These algorithms will be inspired by the successful approach of Software-Defined Networking. Anomaly detection methods will be developed that can ascertain the difference between the expected (modeled) behavior of the system and the observed behavior (from sensors). Anomalies will be detected both at short time-scales, using high-fidelity models, and longer time-scales, using machine learning and statistical-based methods. The detection and classification of anomalies, whether they be random faults or cyber-attacks, will represent a significant contribution, and enable the re-programming of the control systems (through re-routing the parts) to continue production.The manufacturing industry represents a significant fraction of the US GDP, and each manufacturing plant represents a large capital investment. The ability to keep these plants running in the face of inevitable faults and even malicious attacks can improve productivity -- keeping costs low for both manufacturers and consumers. Importantly, these same algorithms can be used to redefine the production routes (and machine programs) when a new part is introduced, or the desired production volume is changed, to maximize profitability for the manufacturing operation .

软件定义控制（SDC）是一种革命性的制造系统控制方法，它使用整个制造系统的全局视图，包括所有物理组件（机器，机器人和待处理的部件）以及网络组件（逻辑控制器，RFID阅读器和网络）。随着制造系统变得越来越复杂，连接性越来越强，它们变得更容易受到小故障的影响，这些小故障可能会导致重大故障，甚至是通过互联网进入工厂的网络攻击。 在这个项目中，网络和物理组件的模型将用于预测制造系统的预期行为。由于制造系统的组件在时间和空间上紧密耦合，这种时间-物理耦合与系统的高保真模型一起允许检测和分类改变系统行为的任何故障或攻击。一旦检测到并识别，系统将计算物理部件通过工厂的新路线，从而避免受影响的位置。这些新的路由将直接下载到与机器和机器人通信的低级控制器，并将保持生产运行（尽管在降低的水平上），即使面对灾难性的故障。这些算法将受到软件定义网络的成功方法的启发。 将开发异常检测方法，可以确定系统的预期（建模）行为和观察到的行为（从传感器）之间的差异。将使用高保真模型在短时间尺度上检测异常，并使用机器学习和基于统计学的方法在较长时间尺度上检测异常。异常的检测和分类，无论是随机故障还是网络攻击，都将是一个重要的贡献，并使控制系统的重新编程（通过重新路由零件）能够继续生产。制造业占美国GDP的很大一部分，每个制造工厂都代表着一笔巨大的资本投资。在不可避免的故障甚至恶意攻击面前保持这些工厂运行的能力可以提高生产率-为制造商和消费者保持低成本。重要的是，当引入新零件或改变所需的生产量时，这些相同的算法可用于重新定义生产路线（和机器程序），以最大限度地提高制造操作的盈利能力。

项目成果

A Requirements Driven Digital Twin Framework: Specification and Opportunities

• DOI: 10.1109/access.2020.3000437
• 发表时间: 2020-01-01
• 期刊: IEEE ACCESS
• 影响因子: 3.9
• 作者: Moyne, James;Qamsane, Yassine;Tilbury, Dawn M.
• 通讯作者: Tilbury, Dawn M.

Categorization of Anomalies in Smart Manufacturing Systems to Support the Selection of Detection Mechanisms

• DOI: 10.1109/lra.2017.2714135
• 发表时间: 2017-06
• 期刊: IEEE Robotics and Automation Letters
• 影响因子: 5.2
• 作者: Felipe Lopez;Miguel Saez;Yuru Shao;Efe C. Balta;J. Moyne;Z. Morley Mao;K. Barton;D. Tilbury

Control-Oriented Modeling and Layer-to-Layer Stability for Fused Deposition Modeling: A Kernel Basis Approach

熔融沉积建模的面向控制的建模和层间稳定性：基于核的方法

• DOI: 10.23919/acc.2019.8814304
• 发表时间: 2019
• 期刊: 2019 American Control Conference
• 作者: Balta, Efe C.;Tilbury, Dawn M.;Barton, Kira
• 通讯作者: Barton, Kira

A Unified Digital Twin Framework for Real-time Monitoring and Evaluation of Smart Manufacturing Systems

• DOI: 10.1109/coase.2019.8843269
• 发表时间: 2019-08
• 期刊: 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)
• 作者: Yassine Qamsane;Chien-Ying Chen;Efe C. Balta;Bin-Chou Kao;Sibin Mohan;J. Moyne;D. Tilbury;K. Barton

Dynamic Rerouting of Cyber-Physical Production Systems in Response to Disruptions Based on SDC Framework

• DOI: 10.23919/acc.2019.8814412
• 发表时间: 2019-07
• 期刊: 2019 American Control Conference (ACC)
• 作者: Yassine Qamsane;Efe C. Balta;J. Moyne;D. Tilbury;K. Barton