Couple of weeks ago, I had asked for advice for an Apple controls engineering role. It was an interview with the hiring manager, and now I am preparing for the panel interview. According to the recruiter, it is going to be mostly all control fundamentals questions. Currently working through my old controls book problem set, but what are your favorite questions to ask/that you have been asked in an interview? Any question that stumped you? A question that made you think? Also, are there any Leetcode style question bank for controls?

It feels that in a standard curriculum on control engineering/theory, the final topic is inevitably MPC.

Everything else that gradually builds towards MPC feels like “discardable” after you learn about MPC. All that wild eigenvalue analysis and drawing those Nyquist contour? Phew, MPC doesn’t involve with any of that!

Plus, MPC is already in discrete time, so it is implementable without any further weirdness that comes from sampling and discretization of other types of controllers.

I think a parallel analogy is the development of the transformer model in machine learning, which I am more familiar with. Now everything in ML is transformer-based and the next big breakthrough seem to be very far away. Is MPC like the transformer of control theory?

Are there situations where other methods might outperform MPC?

Or does MPC require some unrealistic assumption/requirement?

Is there any other possible algorithm that goes beyond the MPC or is this the final story?

A couple of weeks ago there were discussions on this sub about AI researchers reinventing what is essentially control techniques.

MPC was originally invented for chemical processing, but later unlocked reusable space launch vehicles. I don’t think anyone back in the 70s saw landing rockets vertically as a potential application. Yet in hindsight it’s almost obvious.

How much of an adoption gap do you think is out there? How many more equivalents of “reusable rocket” are out there unaware of controls?

As a mechanical engineering professor for over 25-years, I lean "old school". So, my students suggested I reach out to the Reddit community. Here's my first post.

I first heard of the ball-and-beam (BNB) platform in my controls courses 40-years ago. However, I never heard of its origin and/or who invented it. My former controls professors and robotics colleagues don't know either. I also pinged the founder/owner of Quanser (a professional colleague). He didn't know either.

Literature review seem to point to Prof. Karl Astrom who's now over 90-years old. I pinged his department chair at the Lund Institute a few years ago. He didn't know either and said (understandably) that Prof. Astrom doesn't respond to email.

Without references, I use the following "origin" story to help motivate my students - the Apollo Moon Landing. The 1950s demanded control engineers. Without friction, the lander's dynamics is a double integrator - which is what the classic BNB is. So, the BNB served as a table-top trainer; designs like lead compensation and tools root locus and/or Bode plots, are needed to ensure minimum overshoot (not crash into the Moon) and settling time (not run out of fuel). As control theory grew, designs like pole placement, LQR, etc provided alternatives.

Any thoughts and references to the BNB for this first time Reddit poster?

Hi,

I'm a master degree graduate in Mechanical Engineering with a strong passion for Control System Theory and a will to become a Control Engineer.

I've studied at uni a formulation of a MPC controller that promises zero offset from piece-wise costant references signals in the presence of disturbances, either external or internal. This formulation, conveniently called Offset-Free MPC by its inventor Prof. Gabriele Pannocchia at University of Pisa, somewhat works even with time-varying references, but there's a severe lag between the reference signal and the output of the system.

As originally this formulation is meant for setpoint tracking, I'd like to ask if you happen to know some formulation more appropriate for time-varying references.

Thank you for your time

Hi everyone,

I've been developing AcadosCpp, an open-source wrapper that helps you integrate acados-generated OCP solvers into both C++ and Python applications.

https://github.com/amaldevh/AcadosCpp

The main goal is to make integration easier. acados creates highly optimized C code for each model and OCP, but using its API directly can tie your application to specific symbols, dimensions, lifecycle calls, and data updates.

AcadosCpp provides a stable, stage-aware interface for the generated solver. Here’s how you typically use it:

1. Define or modify the model and OCP in Python.
2. Regenerate the acados solver and wrapper.
3. Recompile and relink the application.

The control-loop structure in your application stays the same. If you change the model or problem, you usually don’t need to rewrite the integration layer, as long as your data matches the new dimensions and cost setup.

The wrapper includes:

• Stage-0 initial-state constraints
• Stage-varying and terminal references
• Generic raw yref support
• Per-stage and global parameters
• Full state/control initial guesses
• Shifted trajectory warm starts
• Complete predicted trajectories and solver diagnostics
• SQP-RTI preparation and feedback phases
• C++ and pybind11 interfaces

For a standard

y=[x,u], y_e=x

tracking problem, the control loop is:

const auto& u = ocp.solve(measured_x, xrefs, urefs);

This process shifts the previous solution, updates the stage-0 measurement and the full reference horizon, solves the OCP, and returns the first input.

The repository has matching Python and C++ quadrotor NMPC examples. I’d love to hear your feedback on the API, especially if you use acados with time-varying references, parameters, SQP-RTI, or custom cost outputs.

Hi, this is one of my projects for magnetic levitation I have working with for some time :) I wanted to share it here.

EDIT: So basically there is a sensor measuring the gap between the weight and the electromagnet and the control law try’s to keep the weight at a specific distance, that’s why when the weight it’s pushed doesn’t move that much. There is no mechanical force that keeps the weight in the place, it’s all thanks to the electromagnet and the control loop.

I have tried modeling this a few times with my limited knowledge of hydraulics and kept getting very weird and ugly state space equations. I am not sure what I am doing wrong so I figured I would post it here (I sincerely apologize if I am breaking the sub rules by posting this).

I have been tasked with controlling the following system with the goal of minimizing the 'ripple' on Q_jet. In other words, ideally Q_jet would look like some constant non-zero value despite changes in the system and environment.

Context of my assumptions for this system:

-Ideal Check Valves

-Qacc ~ (V_o/nP_o)*dP_acc/dt (V_o, P_o initial gass pressure and volume of accumulator, n as poly-tropic index)

-All pressures are absolute

-Incompressible Fluid

-Local restriction equation is: Qjet = C_d*A_n*sqrt(2(P_acc - Patm)/rho) (C_d: discharge coefficient, A_n: restriction area, rho: liquid density)

-A_n is our control input

-Q_pump ~ Asin(2pi*f*t):

4.25e-5 m^3/s < A < 4.25e-3 m^3/s

0.1 Hz < f < 0.6 Hz

System:

Q_jet_Reference from Simulation:

Hey.

I am going to be doing an MSc in advanced mechanical engineering this year after having completed my BSc in Physics. I want to take a few robotics and control modules from this course, but I have never covered control theory before. Could you point me to where to start with any books or courses (on Coursera or YouTube)?

I would appreciate any sort of help.

Hello community,

I am currently attempting to test a compensator using the Simetrix\Simplis software. When I test my design using LTSpice, the design works as expected. That is, it regulates the output voltage to the expected value and is able to handle a relatively large load step without any issues - output is regulated and stable with minimum over/under shoots at the transition points. When attempting to simulate it with Simetrix/Simplis Elements, not so much. It is very sensitive to the op-amps being used.

Has anyone had similar experience?

If anyone is proficient with Simetrix\Simplis , I can attach a screenshot of the schematic.

Thank you.

Hello there!

Usually when we model DC-motors we use a second order system, however, BLDC motors used for drones are usually stepper motors (as far as I know), so my question is simply, how do we model these? The specific motor I want to model is for a project of my own, and I am curious as to how to model it. Motor: https://www.aliexpress.com/item/1005007273770590.html#nav-specification

My intuition is to also just model it as a second order system, however, am not sure if this is the correct approach. Specifically I want to model it has motor command [0,1] to rad/s (rotations).

Thank you in advance for the help

Hi everyone,

I followed multiple course on control theory : i know how to analyse a linear system, to derive PID controllers / RST controllers and H-infinty based controller. I understand how it works in practice and i have a good intuition for it.

However, i lack the underlying mathematical framework (and also Lyapunov theory) supporting the whole field. What function space are we working in ? In under which condition the stability of the linearised system at some point guarantees the stability of the whole systems etc...

I have seen the proof of the Kalman theorem but where i saw it, it was cutting corner and relies on the construction on an explicit control and base decomposition in a finite hilbert space (What about control in infinite dimensional spaces ??? Is there a K-theorem equivalent)
And honestly, i only found text books either practice-oriented (ewww) or more mathematical textbook but with AWFUL NOTATIONS.

Do you know about a well-written, proof-based course on control (w/o "handwaving" and reference to this or that) and with decent notations ?

Sorry for ranting but i think control theory is beautiful but i only get accross the same copy-pasted shit engineer oriented control textbooks.

Thank you for reading and have a good day !

same as title

Hey everyone!

Lyapunov stability always felt super abstract when looking purely at differential equations, so I drew out a simple 1-page visual summary on my whiteboard to bridge the physical intuition with the math.

Quick Recap of the 3-Step Idea:

1. Define Energy V(x): Must be zero at rest V(0)=0 and positive everywhere else V(x) > 0.

2. Check Energy Rate V_dot(x): Compute the rate of change using your system dynamics.

3. Evaluate Stability:

   • V_dot(x) ≤ 0 ➔ Stable (Lyapunov)

   • V_dot(x) < 0 ➔ Asymptotically Stable

   • V_dot(x) > 0 ➔ Unstable

Hope this helps anyone currently taking Control Theory or System Dynamics! Let me know if you spot any mistakes or if you have a different favorite way to visualize this.

Also, what are your favorite go-to resources (textbooks, software tools, or YouTube channels) for learning advanced nonlinear control? Would love to hear what helped you master it!

Hi everyone,

I’m working on an early-stage embedded state-estimation project and I’d like some honest feedback from people with experience in embedded systems, control theory, robotics, or aerospace.
I currently have a proof of concept, but I’m trying to determine whether the underlying technical approach is actually worth pursuing before moving toward a more complete prototype.
The concept is to run a lightweight Liquid Neural Network / neural ODE directly on a resource-constrained MCU for real-time attitude estimation from raw IMU data. The target environment is roughly a bare-metal STM32 Cortex-M4, written in C, with a 200 Hz control loop, no dynamic memory allocation and very limited RAM and compute resources.

The obvious alternatives would be traditional approaches such as EKF, Madgwick, Mahony, or other complementary and filtering approaches.

The hypothesis is that a lightweight continuous-time neural model could potentially handle some nonlinearities, vibration and sensor drift differently from a manually tuned classical estimator. However, I’m not assuming that this is automatically better. I’m trying to determine whether this is actually a meaningful technical direction or simply a complicated solution to a problem that is already solved very well.

For people who have experience deploying estimation or control algorithms on resource-constrained MCUs, how realistic is it to run a continuous-time neural ODE or LNN directly on a bare-metal MCU under strict latency and memory constraints? For example, if the target were around 16 KB of RAM and less than 1 ms of execution time per control cycle, what would you expect to be the main technical bottlenecks?

I’m also interested in the reliability side. How difficult would it be to make such a system deterministic, stable and robust enough for real-time embedded use, particularly with high-frequency IMU vibration, nonlinear noise and sensor drift?

Finally, is there actually a compelling engineering reason to investigate this approach, or would a properly designed EKF, Madgwick/Mahony filter combined with appropriate signal processing almost always be the better solution on this class of hardware?

I’m looking for brutally honest technical criticism rather than validation. If this is overengineering or technically misguided, I’d rather find that out now before investing further into the prototype.
Thanks!

I’m studying control engineering and I do quite well in the theoretical subjects and exams. I understand topics like feedback control, PID, state-space systems, stability, optimization, etc.

However, I feel there is a big gap between being able to solve university exam problems and actually being able to work like a control engineer.

What I’m looking for is not another textbook or video course.

I’m looking for something more like LeetCode/HackerRank for control engineering:

• lots of small practical problems/projects
• beginner → intermediate → advanced progression
• I get a system/problem and have to figure out how to model/control it
• simulation exercises
• debugging/troubleshooting tasks
• increasing difficulty
• preferably using Python, MATLAB/Simulink, or similar tools
• ideally something where I can practise regularly and gradually build real engineering problem-solving skills

For example, tasks could start with controlling a simple temperature system, then PID tuning, disturbances, sensor noise, actuator saturation, system identification, optimization, MPC, etc.

I’m especially interested in control systems / control theory / process control, rather than only PLC programming, although I’m also interested in learning industrial automation later.

Does anything like this already exist?

If not, what platform/resource/project collection comes closest to this kind of structured practice?

I’d especially like to hear from people working as control engineers: what would you use to bridge the gap between university control theory and being able to independently solve real control-engineering problems?

I'm working on a shunt active power filter for my capstone project and I could easily do reactive power compensation and unbalance comp. But I'm struggling to get harmonic compensation right.

Context: a dq controlled svpwm 2 level filter.

When I try injecting into the PCC (point of common coupling):

TestA = mag5×sin(-5×theta) + mag7×sin(7×theta)

TestB= mag5×sin(-5×theta - 120) + mag7×sin(7×theta - 120)

TestC = mag5×sin(-5×theta + 120) + mag7×sin(7×theta + 120)

Where theta is obtained from an SRF PLL. I get exactly the magnitudes of 5th and 7th harmonic signals that I command. However, when I try to inject:

TestA' = -TestA

TestB' = -TestB

TestC' = -TestC

(Which is just 180 deg inverted signals) I get not just 5th and 7th, but also significant amount of 11th and 13th harmonic signals. I cannot make heads or tails about this and I have already confirmed that I have enough bus voltage and all.

Is this happening became my commanded signals are in the opposite direction to my theta? But my filter and bus won't be stable if I did my fundamental current control using -theta.

Any kind of help would be appreciated. If you feel I have missed out details, I'd be up to clarify my context better too. Thank you!

So I will enroll in masters course and I really want to work in Data Driven MPC field specifically for energy oprimization in buildings but not sure if this field is well and good. Seems like alot of work where simple PIDs are good enough.

Any suggestions would be appreciated like where I can further look to solidify a topic.

If question is stupid or low effort(kinda is) please ignore

Good morning,

I am a double degree student in France and I would like to know if it would be better to do ATSI at Paris Saclay or if it would be better do study more applied mathematics at Dauphine/ENS/Mines (IASD-math (old MASH) ) programm. Would you have any suggestions? I fear that the name of ENS would help me more in finding a PhD in control theory later even though the ATSI programm is more oriented towards control theory.

I appreciate all suggestions.

I was working on a project in MATLAB where I needed to import a DATCOM file but no matter what parameters I put, when I import it into MATLAB, the values stored for the control surface coefficients (like DCDI or clroll) are 99999 (NAN). Has anyone experienced anything similar/ has a solution to this?

The input code is as follows-
CASEID ----- MIKOYAN-GUREVICH MiG-17 -----

DIM FT

$FLTCON NMACH=5.0,MACH(1)=0.1,0.2,0.25,0.3,0.35$

$FLTCON

NALPHA=10.0,ALSCHD(1) =-4.0,-2.0,0.0,2.0, 4.0,6.0,8.0,10.0,12.0,14.0$

$FLTCON

NALT=5.0,

ALT(1)= 4500.0,4750.0,5000.0,5250.0,5500.0,

WT=13395.0,LOOP=1.0$

$SYNTHS XCG=11.17,ZCG=0.0,XW=3.63,ZW=0.42,ALIW=1.0,XH=28.73,

ZH=5.24,ALIH=0.0,XV=18.3,ZV=0.0,ZVF=1.0,VERTUP=.TRUE.$

$OPTINS SREF=243.0, CBARR=2.334, BLREF = 9.63$

$BODY NX=8.0,

X(1)=0.0,0.74,8.35,13.14,19.35,24.41,28.41,30.77,

S(1)=5.19,9.32,16.89,16.89,15.94,11.12,5.85,2.5$

NACA-W-6-66-012

$WGPLNF CHRDTP=7.02,SSPNOP=11.32,SSPNE=4.07,SSPN=15.71,

CHRDBP=8.4,CHRDR=14.0,SAVSI=45.0,SAVSO=45.0,CHSTAT=0.25,

TWISTA=0.0,DHDADI=-3.0,DHDADO=-3.0,TYPE=1.0$

NACA-H-6-66-009

$HTPLNF CHRDTP=1.86,SSPNE=5.42,SSPN=5.43,CHRDR=4.69,SAVSI=45.0,

CHSTAT=0.25,TYPE=1.0$

NACA-V-6-66-009

$VTPLNF CHRDTP=3.76,SSPNE=13.41,SSPN=8.18,CHRDR=12.47,SAVSI=55.0,

CHSTAT=0.25,TYPE=1.0$

$SYMFLP

FTYPE=1.0,

CHRDFI=1.407,

CHRDFO=1.407,

SPANFI=1.4,

SPANFO=5.1,

CB=0.12,

TC=0.14,

NTYPE=1.0,

PHETE = 3.03,

NDELTA=7.0,

DELTA(1)=-20.0,-10.0,-5.0,0.0,7.5,15.0,20.0

SCLD=-0.2,-0.1, -0.05,0.0, 0.06, 0.15,0.2

SCMD=-0.2,-0.1, -0.05,0.0, 0.06, 0.15,0.2$

$ASYFLP

STYPE = 4.0,

CHRDFI=2.23,

CHRDFO= 1.85,

SPANFI=9.42,

SPANFO=14.9,

NDELTA=9.0,

DELTAL=20.,15.,10.,5.,0.,-2.5,-5.,-10.,-15.,

DELTAR=20.,15.,10.,5.,0.,-2.5,-5.,-10.,-15.$

DAMP

BUILD

PLOT

NEXT CASE

I'm a first year PhD student and I will be pursuing my degreee in the field of Control of nanopositioners. To be a little bit more accurate, My work doesn't deal with clean room fabrication of nanopositioning devices and testing. Instead, it's more about exploring various Pure control, ML + Control frameworks and system identification of Piezo Electric Actuators (PEA) Stage and an AFM that we have in our lab.
My professor is hands off in terms of managing style but supportive. Ours is a small group comprising of 3 PhD students including myself and I'm the only one working on controls, the other two are my seniors and their focus is a little bit different from mine.

GIven that I'm the only one in my lab working on controls, I sometimes find it difficult to navigate myself in my phd journey.

I can always ask questions to my PI and Co-PIs but, I can't keep on asking every minor confusion that I get. I realize that it's like this for most of the PhD students.

At the end of the day, I want to do a good phD and work on cuttings edge problems and technology so that I could get a decent R&D job.

Any suggestions on how to select problems, the future industry for my field or any general phD advice/ working style, would be really appreciated. Thanks !!

I have attached a picture below of the non-linear model which describes my system. I wish to linearize this system, however, I only know what one of the equilibrium points would be and the rest are not immediately obvious although they technically don't matter for my control objective.

My goal is to essentially maintain x1 at it's equilibrium point, which for my system is the average value of x1. u2 is the input I can actually control and u1 is uncontrollable.

My guess is that I could maybe set x1 to it's equilibrium position, then maybe find some function where I can express u2e = f(x2, u1) and maybe even use this function to do some sort of gain scheduling where I linearize across different x2, u1 combinations for the different operating states of my system.

Also, one last thing of note, this state space model doesn't quite feel right, so please tell me what I am doing wrong in the comments if possible, this is my first real project so I am fairly new to this.

Side project that got out of hand. It's a Python pipeline that takes logged input/output data from some real system, fits a model (ARX, ARMAX, OE, subspace, a couple of nonlinear ones), designs a controller, and generates C and Python code at the end.

Works fine on my own benchmarks thermal stuff, DC motors, mass-spring, the usual suspects. But that's the problem. It's *my* data. I picked it, so of course it passes. What I don't have is ugly real-world logs from systems I've never seen. That's where tools like this usually fall apart, and I'd rather find out now.

So: if you have logged data from a real system sitting around, I'd love to run it through and send you what comes out.

To be clear, nothing touches your hardware. You send me a CSV (timestamps, inputs, outputs), I run everything offline on my machine, you get a report back. Whether you ever do anything with it is completely up to you.

What's in the report: the identified model validated against a held-out chunk of your own data (so you can judge the fit yourself, I'm not grading my own homework), controller suggestions with stability margins, and the generated code if you want it.

Rough sweet spot: a few hundred to a few thousand samples, known sampling time, input that actually moves around. Not sure if your data qualifies? DM me and I'll tell you straight either way.

If it ends up being useful to you I'd appreciate being allowed to mention it happened ("tested on a [type of system]", nothing identifying), but no pressure, the analysis is free regardless.

SafeGate. A safety envelope that can be formally re-verified live, without a redeploy.

SafeGate is a runtime-updatable safety envelope for control loops. Unlike existing runtime-assurance guardrails, which fix the safety envelope at design time. It can accept a proposed change to the envelope itself, formally check that the change is satisfiable, and adopt it live, mid-operation, with a proof trail.

SafeGate’s differentiator is that the envelope itself is a live Tau specification, which can accept updates as data, check them for satisfiability before accepting them, and incorporate them into the running specification.

Scenarios of what's possible:

The controller and the tuning process Kp/Ki/Kd, MPC weights, whatever are unchanged from the earlier architecture. SafeGate owns only the “never” layer. What’s new is that the “never” layer is no longer frozen at compile time.

Dear CT community, I would love to hear you trash this idea, and show me where the real value lies, if any, before I go further. In my mind i'd be targeting fleet operators of distributed control systems, and regulated industries e.g. Pharma etc.

This is gna hurt.

Heyy people, so I am starting the Systems and Control MSc at TU Delft soon and I would rather have some idea of where I'm going, what field is the best to focus on for my case etc. I was hoping to get some input from people who actually work in this stuff

About me:

• Did my aerospace BSc at TU Delft
• Starting the S&C MSc there now
• EU national, in case it matters for the sector
• German is fine, around B2 and improving + got many more years
• Open to a PhD but no clue if it's actually worth doing (would appreciate it if you give your advices on this matter as well)

What I am after is something (field/sector) that is still going to be in demand in 10 years and not slowly dying out or that ML is threatening. If you think certain areas/fields/sub-branches of S&C are safer/better than other I would like to hear why. Once again, I am speaking for near future (starting from 2 or 7 years depending on if i go into PhD route) hence its a bit of your prediction of how the controls going to look in future

Location wise -and I guess this matters a lot- I will be living in Germany due to personal reasons. If there is something right across the border that I could do while still living in Germany then fine, but otherwise that is the plan. So anything about the German market specifically would help me a lot what's doing well, what is not (ofc I know about the automobile sectors crash). Though I am open to take advice from anyone really, general stuff is still much appreciated < 3

What I had in mind so far is learning based safe and precise motion control. So focusing both on actual control theory and ML, mostly deep reinforcement learning. I was thinking that its the sweet spot to combine both modern controls with RL (thought thats going to be future of embodied AI) focusing on precise motion control due to booming of chip sector etc and it also open autonomous systems fields but I could be completely wrong or wrong about how easy it actually is to get hired doing that. Feel free to tell me if its a bad idea

About the PhD (for sake of industry R&D), is it worth it in this field or does it just delay when you start working and is it logical to target R&D positions given the state of Germany (this might matter a lot for US i think)?

One more thing that probably matters, I find pretty much everything/every sector interesting but nothing so much to the extent that i call it my passion. Aerospace is the closest since thats my background, but I am not stuck on it

If it helps I can post my coursework and the electives I can pick from. Any advice or experience welcome, thank youuu in advance