Beckhoff Integrates Physical AI into Machine Control Systems

[Cengiz Özemli]

## Beckhoff Integrates Physical Artificial Intelligence into Machine Control Systems

Beckhoff Automation is introducing its innovations at Hannover Messe 2026, integrating physical artificial intelligence (AI) technologies into machine control systems. This technology allows large language models (LLMs) to directly influence real-time machine operations through standardized interfaces.

### What is Physical Artificial Intelligence?
Physical artificial intelligence combines AI with deterministic control systems, enabling machines to go beyond fixed programming logic and respond dynamically to user input and operational context.

### Natural Language Controlled Industrial Automation
Beckhoff integrates LLMs into control environments within the TwinCAT ecosystem. Using TwinCAT CoAgent software and a voice interface, users can generate machine instructions with natural language commands. This approach allows complex automation tasks to be performed without coding knowledge. The control architecture provides structured communication between AI models and industrial control systems through the Model Context Protocol (MCP).

### From Command to Real-Time Machine Operation
Physical AI systems go beyond traditional human-machine interfaces, understanding intent and generating actions based on context. In the demonstration, the control system:

• Translates spoken language into machine commands
• Plans paths for robotic movement
• Executes diagnostic and monitoring functions in real time

This integration provides flexible and adaptable automation flows between the user and machine control.

### Application with Modular Robotic System
The ATRO modular industrial robot system is demonstrated in an application environment, fully controlled by voice commands with TwinCAT CoAgent. The robot plays chess against visitors, showcasing its ability to interpret commands, plan movements, and execute precise actions.

### AI-Powered Lifecycle Integration
Beckhoff's tools, such as TwinCAT Machine Learning Creator, support machine manufacturers throughout the entire lifecycle:

• Design: AI-assisted coding and system configuration
• Operation: Continuous monitoring and data-driven diagnostics
• Optimization: Detection and resolution of performance issues

### Conclusion
The integration of physical artificial intelligence offers more accessible and flexible solutions in automation systems. The combination of deterministic control with AI-powered interpretation allows adaptation to changing needs without reprogramming. According to Hans Beckhoff, this development shows the increasing importance of interoperability, ease of use, and adaptability in industrial automation. This technology, presented at Hannover Messe 2026, paves the way for the integration of natural language interfaces and AI models into industrial systems.