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

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、机器人和要加工的零件)以及网络组件(逻辑控制器、RFID读取器和网络)。随着制造系统变得更加复杂和相互联系，它们变得更容易受到小故障的影响，这些小故障可能会演变为重大故障，甚至是通过互联网进入工厂的网络攻击。在这个项目中，将使用网络组件和物理组件的模型来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都是紧密耦合的，这种时间-物理耦合，加上系统的高保真模型，允许检测和分类任何改变系统行为的故障或攻击。一旦检测到并确定，该系统将计算物理部件通过工厂的新路线，从而避开受影响的位置。这些新的路线将被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(尽管水平降低)，即使面对其他灾难性的故障也是如此。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，以确定系统的预期(建模)行为和(来自传感器的)观察行为之间的差异。将使用高保真模型在短时间尺度上检测异常，使用机器学习和基于统计的方法在较长时间尺度上检测异常。对异常的检测和分类，无论是随机故障还是网络攻击，都将是一项重大贡献，并使控制系统的重新编程(通过重新布线部件)能够继续生产。制造业占美国GDP的很大一部分，每一家制造工厂都代表着一大笔资本投资。在不可避免的故障甚至恶意攻击面前保持这些工厂的运转，可以提高生产率--使制造商和消费者的成本保持在较低水平。重要的是，当引入新零件或更改所需生产量时，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。

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 .