Design and Deploy Control Systems with Agentic AI and Model-Based Design
| Start Time | End Time |
|---|---|
| 24 Sep 2026, 9:00 AM EDT | 24 Sep 2026, 10:00 AM EDT |
| 24 Sep 2026, 2:00 PM EDT | 24 Sep 2026, 3:00 PM EDT |
Overview
Agentic AI tools are changing how engineers approach design tasks. However, for complex engineered systems, an informal, prompt-driven approach to design is not enough.
In this webinar, you will see how to guide an AI agent through Model-Based Design, a widely adopted and trusted way to design and deploy embedded control systems. With the MATLAB® Agentic Toolkit and Simulink® Agentic Toolkit, the workflow begins with a natural language prompt and proceeds through an iterative, structured process. The AI agent:
- Generates requirements
- Designs a model predictive controller (MPC)
- Verifies behavior using closed-loop simulation with a plant model
- Runs Software-in-the-Loop (SIL) and Processor-in-the-Loop (PIL) testing
- Generates embedded code for deployment
As the agent produces standard engineering artifacts like models, controllers, and test results, you inspect, verify, and refine the design at every stage. This approach combines the speed of agentic AI with the rigor and traceability of Model-Based Design.
Highlights
- See an AI agent build and deploy a model predictive controller using Model-Based Design
- Discover how AI-generated models, controllers, and test results remain inspectable, verifiable, and fully integrated into existing engineering workflows
- Learn why guiding AI agents through structured engineering workflows leads to safer and more reliable results
Please allow approximately 45 minutes to attend the presentation and Q&A session. We will be recording this webinar, so if you can't make it for the live broadcast, register and we will send you a link to watch it on-demand.
See more upcoming webinars related to generative and agentic AI.
About the Presenter
Emmanouil Tzorakoleftherakis is a principal product manager at MathWorks, with a focus on reinforcement learning and control systems. Emmanouil has a M.S. and a Ph.D. in mechanical engineering from Northwestern University, specializing in control systems and robotics, and a B.S. in Electrical and Computer Engineering from University of Patras in Greece.
Naren Srivaths Raman is a principal software engineer at MathWorks, with a focus on reinforcement learning and model predictive control. He holds an M.S. and a Ph.D. in Mechanical Engineering from the University of Florida, and a B.E. in Mechanical Engineering from Anna University, India.
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