Looking back through the history of automation, it’s not difficult to see how most advances are extensions of existing technologies. This can be seen at all levels of automation—from the evolution of relays into programmable logic controllers to the enterprise, where the all-encompassing ERP (enterprise resources planning) systems were developed from expansions of material requirements planning (MRP) software over the course of several years.
然而，当您查看自治系统时，前进似乎非常茫茫，传统制造系统中的起源点并不总是容易的。这种差距可以使最终用户威胁自治技术;但是当您追溯自主系统的开发路径时，它可以使这些新技术不那么令人恐吓。
Connecting the dots
AtRockwell AutomationFair 2021, Jordan Reynolds, global director of data science at Kalypso (a Rockwell Automation company), gave a presentation on autonomous systems that helped explain how these advanced neural systems, as they’re applied in industry, can be seen as extensions of the PID (proportional-integral-derivative) closed loop control systems with which we’re all familiar.
Jordan Reynolds, global director of data science at Kalypso (a Rockwell Automation company).Illustrating the evolution from PID control to autonomous systems, Reynolds explained that the first step is to start with a physical system—an entire plant or a production line—and create a model or digital twin of this system that shows how that system responds to changes to inputs or operational parameters, as well as disturbances.
This model, which sets the stage for autonomy, is created via hybrid modeling. Reynolds said hybrid modeling is developed through two processes: first, there is input from an engineer followed by input from a data scientist who understands AI (artificial intelligence).
“三角洲之间的工程师知道一个d what a data scientist does to derive a model that can learn rather than being programmed,” said Reynolds. He stressed that, to develop an effective model for autonomous operations, you need the engineer to define basic principles and have the data scientist close out the gaps to ensure the model performs to standards. “This is hybrid modeling,” he said.
Autonomic control
Rockwell Automation is focusing on this area because it views autonomy problems as control problems.
“前馈控制是控制控制的第一预测应用之一，”雷诺兹说。“它扩展了反馈控制，并提供了尚未到来的状态的早期迹象，以便可以主动解决它。模型预测控制（MPC）是一种现代版本的反馈控制，您可以使用多变量模型来表征系统如何执行。我们使用这些模型来控制系统比我们的PID更好。“
Reynolds noted that Rockwell Automation acquired Pavilion Technologies in 2007 for its MPC technology.
“MPC is good with highly predictive systems that don’t change that much,” said Reynolds. “But it falls short when recipes or parameters change. The MPC must be updated because it does not adapt on its own. That’s where adaptive control comes in. In adaptive control, the model doesn’t have to be perfect because it can adjust to changes.”
Reynolds' presentation featured a slide explaining Reinforcement Learning within an industrial production construct.加强学习是一种更新的概念，可扩展自适应控制，是一种具有云计算资源提供的能力以及植物地板系统之间更大的连接的能力的新兴方法。
Explaining the connections between PID and autonomous operations with Reinforcement Learning, Reynolds said PID is used to ensure the system complies to the setpoints, whereas MPC determines the setpoints based on the multiple inputs and outputs of the system. Going further, Reinforcement Learning creates an executive function that can strategize.
Reynolds provided a car racing example to help illustrate this concept. MPC keeps the car in the lane, he said, while Reinforcement Learning helps you strategize about how to win the race, e.g., when to change tires, when to take the inside lane, etc.
Deep learning neural networks, like those used in Reinforcement Learning, is where most innovation has come from as industry advances toward greater use of autonomous systems. The problem is that, even though neural networks are highly accurate, they’re not very transparent or explainable. “An engineer can’t easily get an answer as to why decisions are made by the neural network,” Reynolds said.
这就是为什么罗克韦尔自动化侧重于深入象征的回归来解释AI控制模型如何工作。Reynolds表示，Rockwell Automation在其上使用它FactoryTalk Analytics LogixAI.According to Rockwell Automation, FactoryTalk Analytics LogixAI applies analytics within the controller application to achieve process improvement. It is an add-on module for ControlLogix controllers that streams controller data over the backplane to build predictive models.
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了解有关正在做的工作的更多信息AI决定更透明和解释. |
