SciEngineer

Agentic AI is starting to change how we think about model-based engineering workflows.

Our engineering colleague, Bence Nagy, recently tested the new Simulink Agentic Toolkit on a real engineering task: DC motor modelling and control verification.

The goal was to build a validated Simscape model of a DC motor with H-bridge control for speed and direction testing.

In preparation, he also followed the official MathWorks guide on Agentic AI with MATLAB, see it in the first comment.

The result was surprising in its simplicity and speed:

In less than 20 minutes, a working Simscape model was generated that:
• matched datasheet behaviour (~2200 RPM no-load speed)
• supported CW and CCW direction control
• responded correctly to PWM inputs (~1100 RPM at 50% duty cycle)
• was ready for control algorithm testing

What stood out most was not only the speed of generation, but that the first model already worked without extensive debugging.

This is an interesting early example of how AI-assisted engineering workflows may support faster prototyping and validation in model-based engineering and automotive software development.

🚀 The Cassini Hackathon has officially kicked off!

We’re proud to be sponsoring this event, bringing together innovators, engineers, and researchers to tackle real-world challenges using space data.

There’s huge potential for innovation in this space. From protecting our water resources to building the next generation of environmental start-ups, the opportunities are just beginning.

A big thank you to the organizers for screating such a meaningful and forward-looking platform.

Best of luck to all participants—we’re excited to see your ideas take shape! 🌍