The integration of edge automation and artificial intelligence redefines the role of the PLC, transforming it from a simple controller into an intelligent node capable of real-time processing, overcoming the limits of traditional PID control and improving efficiency and adaptability.
The topic discussed in this article is part of the “Smart Growth” EU project, focused on the control of a thermal process (crystal growth for laser applications and high performances oven for cooking pizza) managed by a PLC connected to a network of new generation sensors and enhanced database; in this context, advanced deep learning algorithms are used to improve control performance, enabling adaptive and data-driven process management directly at the edge.
In recent years, the evolution of industrial automation has seen a growing convergence between traditional control systems and advanced artificial intelligence technologies.
In this context, the PLC is no longer just a device dedicated to deterministic input/output management, but is increasingly becoming an intelligent node within edge automation architectures.
The concept of edge automation is based on processing data close to its source, directly in the field, reducing latency and dependence on cloud infrastructures. In this scenario, the PLC plays a central role thanks to its capability to operate in real time, ensuring deterministic scan cycles and immediate responses to process events. This makes it an ideal candidate for integrating AI algorithms that require consistent and repeatable response times.
Traditionally, industrial process control has relied on PID (Proportional-Integral-Derivative) controllers, which are robust and well-established but often complex to fine-tune, especially in the presence of non-linear systems or processes subject to dynamic variations. Tuning a PID controller requires specific expertise and can prove ineffective when the process exhibits behaviors that are difficult to model using classical mathematical approaches.
The introduction of artificial intelligence techniques, such as machine learning and neural networks, makes it possible to overcome these limitations. These algorithms can learn directly from process data, automatically adapting to changes and improving their performance over time. When integrated at the edge level, within or near the PLC, these models can operate in synergy with traditional control logic, providing faster and more context-aware decisions.
A key aspect is precisely the integration between the PLC’s real-time capabilities and AI processing. While cloud systems offer high computational power, they are not always able to guarantee the timing constraints required by critical industrial applications. Local execution of algorithms, on the other hand, makes it possible to maintain direct control over the process, avoiding communication delays and improving overall system reliability.
Furthermore, the use of artificial intelligence at the edge opens up new possibilities in terms of predictive maintenance, energy optimization, and adaptive control. The PLC thus becomes a key element not only for automation but also for the digitalization of production processes, contributing to the development of increasingly autonomous systems.
In conclusion, the integration of edge automation and AI represents a natural evolution of the PLC’s role. From a deterministic controller to an intelligent platform, the PLC stands at the center of a new generation of industrial systems, where the ability to process data in real time and dynamically adapt to operating conditions provides a fundamental competitive advantage.
Written by Cibolabs