PLCs and AI Controllers (Artificial Intelligence) represent two distinct approaches to industrial process automation and control. Selecting the most suitable approach will depend on the user's current needs. While both aim to optimize processes, their capabilities, applications, and architectures have key differences, which are summarized below:
· PLCs:
- Traditional
PLCs execute deterministic, rule-based logic (e.g., ladder logic) for tasks
like conveyor control or motor sequencing. They excel in real-time reliability
and safety-critical operations due to their predictable response times.
- Its response capacity is limited to scenarios foreseen in its programming, without autonomous adaptation to unscheduled changes.
· AI Controllers:
- These systems
leverage machine learning (ML), predictive analytics, and adaptive algorithms
to optimize processes dynamically. They handle nonlinear systems (e.g.,
chemical processes) by adjusting real-time parameters based on sensor data.
· PLCs:
- Work with pre-programmed logic and fixed sequences, executing repetitive tasks based on defined rules. For example, controlling the synchronization of an assembly line or activating emergency stops.
· AI Controllers:
- Make autonomous decisions for unforeseen scenarios based on historical patterns, as
well as on digital twins, improving efficiency and reducing downtime.
- Use machine
learning algorithms to analyze data in real time and dynamically optimize
processes without manual reprogramming.
Key algorithm
references:
-. LSTM neural networks for compressor failure
prediction, increasing accuracy in early detection
-. Distributed Machine Learning and Federated
Learning for multi-plant predictive maintenance to reduce data transfer.
-. Q-learning adaptive control in adjusting control
elements (e.g. pneumatic valves)
· PLCs:
- Efficient in
structured and repetitive processes, such as motor control or assembly line
synchronization. Programs are based on standard languages (ladder logic) and can
integrate modules for specific tasks (e.g. PID control). (2)
- Limited in managing non-repetitive tasks or those that require continuous adjustments, such as polishing parts with dimensional variations.
· AI Controllers:
- Capable of
managing non-linear and dynamic systems. For example, in production lines where
demand fluctuates, they automatically adjust routes using data from IoT
sensors.
- Integrates
digital twins to simulate scenarios and optimize processes before physically
implementing them.
- Applies
artificial vision for quality inspection, detecting defects in products with
greater precision than traditional methods.
- Learn from
data generated by IoT sensors, allowing production routes to be optimized or
speeds to be adjusted according to demand.
|
PLCs |
AI Controllers |
|
Deterministic performance (critical for safety) (1), (2) |
Adaptive decision-making (e.g., self-learning control loops) (3), (4) |
|
Ease of programming (ladder logic) (1) |
Predictive capabilities (e.g., maintenance, energy optimization) (3),
(5) |
|
Proven reliability in harsh environments (2) |
Handling complex data (e.g., anomaly detection via computer vision) (3),
(9) |
· PLCs:
- Requires scheduled maintenance and manual fault diagnosis. Its robustness makes it resistant to harsh environments (dust, vibrations), but it relies on human intervention to resolve unforeseen problems.
· AI Controllers:
- Implement self-diagnosis, advanced predictive maintenance, and proactive correction. For example, AI systems that identify wear and tear on machines and schedule maintenance before failures occur. They automatically correct operating parameters, reducing the dependence on specialized technicians, as algorithms can "learn" from past operations and adjust parameters automatically.
6.
Applications
· PLCs:
- Fixed automation tasks (e.g., conveyor
belts, assembly lines).
- Safety interlocks and emergency shutdowns. (1), (2)
· AI Controllers:
- Predictive maintenance: Analyzing vibration or temperature trends to preempt failures. (3), (5)
- Process optimization: Dynamically adjusting parameters in chemical reactors for energy efficiency. (3), (4)
- Human-machine collaboration: NLP interfaces for voice-controlled adjustments. (2), (3)
7. Technological Synergies
The future lies
in hybrid systems combining PLCs with AI:
- Edge
computing: PLCs process local data for low-latency control, while AI models run
on edge devices for real-time analytics. (2), (6), (7)
- Digital twins: Virtual replicas simulate scenarios (e.g., equipment failure) to refine PLC logic and AI predictions. (2), (4)
- 5G/IIoT: High-speed connectivity enables distributed AI controllers to coordinate with PLCs across facilities. (6), (7)
8.
Scalability and costs
· PLCs:
- Moderate
initial costs, ideal for specific applications. However, scaling functions require
additional hardware (e.g., I/O modules) .
- Cost-effective in stable processes where investment in AI is not justified.
· AI Controllers:
- Higher initial investment, but they offer scalability through software updates. For example, adding an optimization algorithm without changing hardware.
· PLCs:
- Limited scalability and integration with modern IT systems (e.g., MES/ERP). (2)
- Inflexibility in adapting to dynamic processes. (2)
· AI Controllers:
- Data dependency: Requires high-quality, labeled datasets, which legacy systems often lack. (2), (3)
- Cybersecurity risks: Increased attack surfaces due to interconnected systems. (8-7),
- Skill gaps: Engineers need expertise in both PLC programming and ML tools. (3), (5)
- AI hallucinations due to insufficient model training, poor data retrieval, or other underlying deficiencies.
10. Future Trends
- Autonomous factories: AI-driven PLCs will enable self-optimizing production lines with minimal human input.
- AI code generation: Tools like Gemini 2.0 Flash automate ladder logic creation, cutting programming time by 30%. (1)
- Ethical AI: Transparent decision-making frameworks to ensure accountability in autonomous systems. (2), (4)
- Sustainable automation: AI prioritizes energy efficiency and waste reduction (e.g., Gallium Nitride "GaN"-based components). (4), (5)
11. Conclusion
PLCs remain indispensable for
deterministic tasks, while AI controllers unlock adaptability and predictive
power. PLCs are ideal for harsh environments and critical applications that
require stability and real-time responses and remain relevant in low-risk,
critical applications. On the other hand, AI controllers revolutionize the
industry with adaptability, predictive analytics, and the ability to manage
complexity. The choice depends on factors such as the required flexibility,
budget, and the need for integration with advanced technologies. Companies
adopting hybrid systems will lead in efficiency, sustainability, and
resilience.
References:
- PLC Programming: Traditional vs AI -Which Wins? https://accautomation.ca/plc-programming-traditional-vs-ai-which-wins/
- Ai Plc Smart Industrial Zones. https://zeroinstrument.com/ai-plc-smart-industrial-zones/
- How AI can be applied to program PLCs in industrial automation. https://antomatix.com/how-ai-can-be-applied-to-program-plcs-in-industrial-automation/
- From optimization to autonomy - Top five manufacturing automation trends for 2025 from OMRON. https://industrial.omron.eu/en/news-discover/blog/from-optimization-to-autonomy-top-five-manufacturing-automation-trends-for-2025-from-omron
- Industrial Automation Trends 2025. https://www.piglerautomation.com/industrial-automation-trends-2025/
- 8 Key Industrial Automation Trends in 2025. https://www.rockwellautomation.com/en-us/company/news/the-journal/8-key-industrial-automation-trends-in-2025.html
- Top 10 Industrial Automation Trends in 2025. https://www.startus-insights.com/innovators-guide/industrial-automation-trends/
- Top 10 Industrial Automation Trends to Watch in 2025. https://industrialautomationco.com/blogs/news/top-10-industrial-automation-trends-to-watch-in-2025
- The Future of Industrial Automation. https://www.industrialautomationindia.in/articles/industrial-automation-trends-2025-ai-ml-smart-manufacturing
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