u/Barnowl93

Hey all,

At ECC earlier this year I had few good chats around CBFs. I get the theory, but I've not really used them before for any application and I need that to wrap my head around them a bit better.

A) Does anyone have some good resources (preferably with code / simulink block diagrams) of CBF applications?

B) what have you used CBFs for? As in, what applications?

For context: I'm not an academic, but I have a PhD in controls and I work at the interface of control theory and engineering practice. I also get to deliver controls lectures at UK universities fairly often.

I ask the students: who here wants to be a controls engineer? Maybe 5%. On a good day (and probably some are just being polite). The rest are students who'll touch control in this course and never again.

But the courses are often built for that 5% (albeit less and less nowadays!). The other 95% just sit through it, and leave without the one thing that would've helped them most: the ability to look at a problem and think "oh that's an MPC-shaped problem." Or a feedforward one. Or just identifying the loops.

And this bites us later. I keep meeting engineers either "in the wild" or in multidisciplinary conferences who have a control problem and don't know it. This is my pet peeve. They'll hand-tune a tower of if-statements, stack heuristics until it sort of works, or throw a neural net at something MPC would've handled cleanly.

They're definitely smart folks, but they just never learned to see the shape of these problems, so they don't reach for the tool that fits.

Here's the pitch I actually make to students. I tell them: most of you won't decide to become control engineers and that's fine. But studying control will make you a better engineer regardless. It's the only course that: 1) makes you think in systems 2) allows you to work with multiple disciplines 3) and if you deploy your controllers you'll get to interact with real engineering systems. This is where we'd talk about a multidisciplinary example, EVs, wind turbine, aircraft, depending on the class.

I'm not suggesting the fix is dumbing it down. Just having "recognition" as the goal for most and "mastery" for that 5% is sufficiently good? Think of Brian Douglas' map of controls... Is just knowing the different parts of the map exist enough for most?

Anyway, those who actually teach (rather than sometimes cosplay): how do you make sure our way of seeing problems sticks years after the exam?

We've seen a few posts here along the lines of "my Simulink model doesn't match my data" or "I modelled X but can't get the experiments to agree"... And similarly from students, "how do I find the parameters of my motor/ battery/ bioreactor?"

Aycan Hacioglu and I wrote a blog about this, using a bioreactor brewing beer as the example. But the same idea can be applied to any system of interest, alongside relevant experimental data.

This is what we are discussing: 1) build a model that captures the overall behaviour, 2) run a sensitivity analysis to identify which parameters actually drive your outputs (not all of them do), 3) then use Simulink Design Optimisation to tune the important ones against your experimental data.

If you want to read more about it, here's the blog: https://blogs.mathworks.com/engineering/2026/06/18/on-parameter-estimation-sensitivity-analysis-and-design-optimization/

https://preview.redd.it/i0ocbyp8zu8h1.png?width=974&format=png&auto=webp&s=4e7fa81353d11c504497f7f3cc53b0bda1bbd021

Hi all,

I do a lot of work with academic and industry partners in engineering applications.

Therefore I end up having a lot of conversations with people around agentic AI for engineering. Specifically using agents as orchestrators for engineering tools (via MCP) .

I find that a lot of folks in the academic space (especially early careers) do not value agentic AI much... Some have still notions of chat bots. Meanwhile industry folks tend to be a lot more cutting edge and enthusiastic about it.. Very much a go go go mentality.

Is that something that you've seen too? How are agents perceived in your space?

A couple of days ago, there was this post on people who are using LLMs instead of controllers. Obviously, that's a terrible idea (even for a tech-bro!).

In the past few months, I've been working a lot with agentic AI alongside MATLAB and Simulink (using the MCP server & more recently the Toolkits). Granted, mostly for system-level stuff & given system architecture and requirements.

Currently playing around using Claude code + MCP server with MATLAB and Simulink for exploring different control strategies for a given problem-- (i.e., Claude code as an orchestrator with MATLAB & Simulink still doing the "proper" work).

I've found this approach quite good *given user knowledge, discipline & imposed structure*. Is that something others have experimented with? What's your experience been like?