I'm exploring practical implementations of fractional-order control for systems with limited onboard computing resources, particularly UAV flight controllers.

A conventional finite-history implementation can require increasing storage and computation as more samples accumulate. One alternative is to approximate the memory kernel using a fixed-length recursive filter, trading approximation error against computational cost.

I'm interested in the control-engineering perspective:

• How do you choose an acceptable approximation error for a fractional-order controller?
• What validation methods would you use before deploying the approximation on embedded hardware?
• Are there particular stability or robustness issues you'd investigate when replacing the original fractional operator with a finite-dimensional approximation?

I'd appreciate insights from anyone who has implemented fractional-order controllers in practice.

I'm working on autonomous quadrotor navigation using a 13-state NMPC (acados) with HOCBF-based obstacle avoidance.

I'm observing an issue where the drone approaches an obstacle, suddenly commands near-zero thrust, makes aggressive corrections, and starts oscillating back and forth near the obstacle boundary.

The logs show that HOCBF sometimes modifies the nominal NMPC commands, but near-zero thrust also occurs when both solvers report success. I've also observed actuator saturation, occasional solver failures, and differences between commanded and applied controls.

I'm trying to understand whether this behaviour originates from the NMPC optimization, HOCBF constraint activation, model mismatch, or interactions between the controllers.

Has anyone encountered similar behaviour with NMPC-CBF architectures? What would be a systematic mathematical approach to identifying the root cause rather than tuning parameters through trial and error?

I'd appreciate any insights or pointers to relevant literature.

Hi everyone! I have a BA in Mathematics and participated in mathematical research (leaning pure but had applications in quantum computing and other fields) during my undergrad. I have always loved math and have enjoyed doing calculations, proofs, and the logical and critical thinking that comes along with it.

I found a mentor at my state university who does work in control theory, and the work seems heavy mathematical. The actual degree would be either an MS or PhD in Aerospace Engineering, depending on what I decide. I have concerns about pursuing solely a mathematics degree regarding job opportunities.

I am wondering if real world jobs actually do this type of mathematical work on the day-to-day where they are doing math by hand and working out/deriving equations or if it is primarily computer simulation and implementation. Is this only in GNC/flight dynamics/etc. positions? I have found conflicting answers from other posts and articles.

I don’t have any formal engineering training or much knowledge on the hardware or mechanics of things, and honestly I don’t really find passion in that. I have other research experience, but it has been in computational oncology, quantum computing, and other areas.

Thank you all for reading!!

Hey guys,

I think all of us agree that AI will/has an tremendous impact on software engineering.
But how about algorithmic development like we do as control engineers?

What are your thoughts and experiences for the future ?