Abstract
With the rapid development of industrial automation technology, PLC (Programmable Logic Controller) programming is undergoing unprecedented transformation. AI-assisted programming, low-code platforms, and the deep integration of IT and OT are enabling industrial controllers to move beyond traditional control logic and gradually align with modern software development concepts. This shift is transforming development models, improving engineering efficiency, and profoundly impacting the flexibility and maintainability of enterprise automation systems.
From Traditional Coding to Low-Code and Modular Programming
Traditional PLC programming primarily relies on the IEC-61131-3 standard, implementing control logic through ladder diagrams, function blocks, or structured text. While mature, this approach presents challenges such as long development cycles, high maintenance costs, and demanding programming skills when faced with complex systems and high efficiency requirements.
With the rise of low-code graphical programming environments, PLC development is moving towards modularization and object-oriented programming. Pre-packaged control objects and modular structures encapsulate basic control details, allowing programmers to focus on higher-level system strategy design. By instantiating and parameterizing object libraries, complex control systems can be quickly built, achieving reusable and standardized control logic. This not only lowers the coding barrier but also enables engineers to more quickly adapt to new automation requirements.
Application of Generative AI in Industrial Programming
AI is becoming a crucial tool in industrial programming. Generative AI (GenAI) and intelligent engineering assistants can provide automatic code completion, module recommendations, and knowledge preservation, helping companies fill the skills gap left by experienced programmers as they retire. AI tools can reduce repetitive work, improve development efficiency, and minimize human error, allowing engineers to focus on complex problem solving and optimization strategies.
Combined with standardized data models (such as OPC-UA and MQTT), AI tools can also enhance system interoperability and smoother data flow between industrial controllers, SCADA systems, and IIoT platforms. This means industrial systems can more easily collect, analyze, and remotely monitor data, providing companies with more efficient automation management solutions.
Maintaining and Modernizing Legacy Systems
Industrial controllers inevitably face hardware and software aging issues over the long term. Legacy systems may be discontinued, and their development environments may be incompatible with modern IT architectures, increasing maintenance difficulties. To address these challenges, companies typically upgrade controllers in phases and introduce modern engineering tools to ensure performance improvements and ongoing support.
At the same time, the loss of experience and knowledge is a major maintenance risk. System design experience and maintenance know-how are often concentrated in the hands of a few experienced engineers. When these engineers leave, insufficient knowledge transfer can lead to system maintenance difficulties. Companies should establish knowledge management mechanisms through documentation, training, and standardized processes to ensure that new engineers can quickly transition to legacy systems and maintain system security and stability during the migration process.
Modular Programming and Reusable Code Practices
Modular programming is a key approach to improving PLC development efficiency and system maintainability. By breaking down engineering projects into function blocks, libraries, and templates, companies can establish standardized modules to achieve code reuse, manage dependencies, and optimize resources. This approach not only speeds up development but also reduces maintenance costs and the potential for errors.
Modular programming also enhances cross-system compatibility. Combined with modules that support cross-platform communication protocols, integration between industrial controllers and SCADA systems and IIoT platforms is more efficient and reliable. At the same time, modularization strategies help engineering teams establish unified development standards within the company, improving overall engineering quality and the flexibility of automation systems.
Programming Revolution Driven by Artificial Intelligence and IIoT
The Industrial Internet of Things (IIoT) and AI are driving PLC programming towards openness, connectivity, and data-driven development. AI-assisted engineering tools can automate repetitive tasks, intelligently recommend control modules, and verify code quality, freeing up engineers' creativity and allowing them to focus on complex strategies and optimization tasks.
In addition, when selecting controllers and development environments, companies need to consider both technical requirements and team skills. For example, if a team is primarily composed of traditional automation engineers, OT-oriented development tools are suitable; if a team is familiar with IT software development mechanisms, an IT-oriented engineering platform is more suitable. By properly matching tools and talent, companies can maximize development efficiency and improve the sustainability and security of industrial automation systems.
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Conclusion
Industrial controller programming is evolving from traditional coding to intelligent, low-code, and modular approaches. The application of generative AI, modular development, standardized data models, and modern controllers not only improves development efficiency and system security, but also alleviates the pressure caused by labor shortages. Future PLC development will be more open, interconnected, and data-driven, making industrial automation systems more flexible, efficient, and sustainable. When planning automation system upgrades and team building, companies should fully leverage AI and low-code tools to promote the development of intelligent manufacturing and lay the foundation for competitiveness in the era of industrial automation.