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When you walk into a traditional factory, the most common name you hear about control systems is undoubtedly "PLC." This control core, born in the 1970s and dominated by ladder diagrams, remains the soul of many production lines. But in this era of data-driven, AI-powered, and cloud-managed systems, can PLCs truly still reign supreme?
More and more control solutions are challenging the dominance of PLCs, such as logic control developed in Python, smart devices paired with edge computing (Edge AI), and even No-Code platforms that are easy for general engineers to operate. Can these new technologies truly replace the seemingly outdated yet rock-solid PLC? In this article, we'll disassemble and compare them!
Classic and Limitations of PLCs
PLCs (Programmable Logic Controllers) can operate stably in extreme environments, have high anti-interference capabilities, fast response speeds, and rigorous programming structures, making them the standard solution for mechanical control. Even after decades, they remain the first choice for applications such as conveyor belt control, packaging machinery, and lifting systems.
However, PLCs also have obvious limitations:
- Program development relies on specific software and syntax, such as Ladder or ST languages.
- It struggles to process large amounts of unstructured data, such as images and sound.
- Integration with cloud and AI systems is not as flexible as with IPCs (industrial PCs).
- With a growing shortage of development talent, young engineers are showing less and less interest in the Ladder programming language.
The Rise of AI Controllers and Edge Computing
In recent years, some industries have begun to introduce AI Edge ControllerThese controllers have AI modules that can make inferences in real time at the endpoint, for example:
- Defect Image RecognitionCamera images are used in real time by an AI model to identify defective products, and the controller simultaneously controls the rejection mechanism.
- Vibration data analysisAI models learn abnormal vibration characteristics of equipment to provide early warnings of malfunctions.
Compared to PLCs, AI controllers excel at handling scenarios with non-traditional digital logic. Many of these controllers have built-in Python or C++ support, and can even be paired with platforms like Jetson Nano and NPUs to handle complex logic without needing to access PLC syntax.
Python Control and Open Source Hardware: Easy to Learn, Highly Flexible
For the education sector and small-scale automation projects, Python + GPIO control boards (such as Raspberry Pi and Arduino) have gradually become the alternative to PLCs. For example:
- Using Python to control the Relay module's start motor
- Object tracking using OpenCV for automatic control of mechanism movements
- Use Flask to create a simple web interface for remote machine operation.
While such solutions may face stability or security issues in an industrial environment, their low barriers to entry and development flexibility make them highly attractive in the R&D stage, POC verification, or educational settings.
No-Code / Low-Code Tools: Ushering in an Era of Universal Automation
Traditional automation development has a high barrier to entry, making it difficult for those without a computer science background to get started. However, more and more... No-Code Platform Lowering the barrier to entry, for example:
- Node-RED: Establish MQTT and Modbus data flow using graphical flowcharts
- Ignition(Inductive Automation): SCADA + control logic platform; data-driven logic can be created simply by dragging and dropping blocks.
- Siemens MindSphere / Beckhoff TwinCAT AnalyticsBuilt-in cloud-based and visual logic flow creation tools
These tools not only enable traditional engineers to accelerate development, but also allow more process engineers to participate in control logic design, thereby achieving true "digital transformation of manufacturing".
Control Scheme Comparison Table
| Control method | stability | Logic processing | Data integration | Programming threshold | Suitable scenarios |
|---|---|---|---|---|---|
| PLC | ★★★★★ | 中 | 低 | Medium and high | Standard equipment with high process stability requirements |
| AI Controller | ★★★★☆ | 高 | 高 | 高 | Intelligent identification, predictive maintenance, and automated quality inspection |
| Python + GPIO | ★★☆☆☆ | 高 | 高 | 低 | Small-scale automation, education, and testing facilities |
| No-Code Tools | ★★★☆☆ | Medium and high | 高 | 低 | Cross-departmental applications, automated reports, visualization |
| Industrial PC + SCADA | ★★★★☆ | 高 | 高 | Medium and high | Large-scale system integration and cloud platform linkage |
Will PLCs become obsolete?
Rather than saying "PLCs will be obsolete," it's more accurate to say their role is being redefined. For critical control requiring extreme stability and long-term operation, PLCs remain the preferred choice; however, in data integration, cloud connectivity, and AI applications, PLCs are gradually giving way to more flexible and faster-development alternatives. For businesses and engineers, the real key isn't which controller to choose, but rather: do your processes, data requirements, and human resources support this shift?
The industrial control world of the future will not only consist of PLCs or AI controllers, but these tools will coexist and thrive, forming a multi-layered intelligent manufacturing architecture.
