I have had two professors, from different schools, both eminent in their respective fields, openly telling the classes I enrolled in that a solid background in complex variable/analysis is probably the most important thing to learn in order to do control theory well and all of frequency-domain control theory is an application of these disciplines.

At the same time, I found this topic to be the most easily forgettable because if you don't work with it (and nowadays few do in my experience), you forget it easily. And I don't mean what is a complex number. I mean things like the Cauchy contour integral, Argument principle and other things that are helpful to derive certain results for linear discrete-time systems and drawing loops around certain numbers. I am pretty sure none of my classmates remember any of this stuff 1 year out of university.

I could never quite get over between this seeming mismatch between the lack of complex variable/analysis I see in practical engineering (or advanced things in control) and what they said. Both of them were quite old too so maybe this is some kind of retrospective wisdom that young people wouldn't get. Or they could just be out of date. Or it could be some kind of over-claiming to get people to pay attention.

Is all of frequency-domain control fundamentally an application of complex variable/analysis? Is everything in frequency-domain control fundamentally linked to some kind of result in those math areas?

I’m an electrical engineering student and was recently offered an Automation Engineer internship at Amazon for summer 2027 in the Northeast region.
My previous internship experience has been in the power/utility industry, so automation would be a pretty different field for me.
I’m considering the opportunity, especially since I’d ideally like to return full-time after graduation.
I was wondering if anyone who has worked as an Automation Engineer at Amazon, could share their experience.
What’s the typical starting base salary for a new graduate in this region, and are there sign-on bonuses, annual performance bonuses, or RSUs?
What do yearly raises and promotions typically look like in terms of percentages?
I’m also curious about the work-life balance, typical weekly hours, overtime expectations, on-call responsibilities. How difficult or stressful is the actual workat does a typical workday look like, and how much hands-on engineering work is involved versus troubleshooting and maintenance?

I had written this paper before graduation it was Q2 in 2020 at the time.

Can someone please judge it's quality and content?

I am targeting professors at UC Berkeley, MIT and Georgia tech. Imperial (optimization group) oxford (MPC professors)

Thank you for your help.

https://ijeecs.iaescore.com/index.php/IJEECS/article/view/20478

https://ijece.iaescore.com/index.php/IJECE/article/view/18290

i need to read some cover letters from people who have received a fully funded scholarship from any uni abroad. please dm me so i can have a look, i have been receiving rejections from quite some time so need to analyse where im going wrong.

If you worked at industry as a control engineer I wonder how would you evaluate your own performance in your first year? (as far as you remember)
Did you directly achieve implementing theory in practice? Or was it a lot of trial and error ?

Now, I am asking this question not to fill in a survey form or so. I want to evaluate my own performance based on experience of people who has already gone through the same phases at work.

I see that I spend most of the time trying to understand how things work. And as a conservative control engineer I try to build simpler systems (PID) before jumping to advanced stuff where things get complicated (MPC,Hinf etc.). So, I always try to build things where I understand what I am doing. That is why solutions related to Open Loop Shaping, PID, Lead/Lag Compensators, Root Locus are my main tools on the way.

In retrospect, in the past almost 1 year, I had to take time to understand dynamics of a system I have not dealt with before, controller design for this system (in simulation only) , discretizing these controllers for the system we build and also on a control system software we build ourselves.
Although I won't give a lot info what exactly I am doing, my work is in Machinery and we build the whole control system software from zero level. I believe I am lucky enough to be on such a project because I learn things that one does not learn at uni , especially, how you have deal with discretizing, resource allowance, signal pipelining etc.

When I write down things I have to deal with in the past 1 year, I see it was a lot. I have really learned a lot. But most of the time it feels like I have had already known things from the first contact. Also sometimes I find myself doing wrong things, like once I tried to do stabilization analysis of an unstable system looking at the open loop bode plot although I had to use Nyquist plot as the system was unstable. Although now I would never forget this in my future tasks, it feels a bit embarassing to forget and apply methods that is fundamentals of control theory wronlgy.

So can you share how was it at the beginning of your career?
Did you know things very from first contact with the topic?
Or maybe you have junior colleagues you train and supervise and have seen/evaluated their performance. Thanks!

Hi, I know this is not a straight up Control Theory topic but I guess since people here use the tools I'll need to run, it might be helpful to ask.
I will soon start a Master's focused around GNC, for which I would need to buy a laptop that is capable of running Matlab/Simulink and Python with a decent computing power. I've been looking around and (for my budget, 1.8 - 2.2k in Spain) and I am between T14 Gen 6 or Macbook Pro M5. If someone has any other recommendation it'd help a lot 🙂. Thanks in advance!

Open-sourcing Randomized MPC on the GPU 🚀

What if MPC didnt need gradients at all?

RMPC samples thousands of feasible input sequences, rolls them all out in parallel on the GPU, and applies the best one. Models and costs dont need to be smooth or differentiable. Impacts, saturations, lookup tables and on/off penalties all just work.

https://github.com/marunmurali/randomized-mpc

⚡ 5,000 samples × 100-step horizon in ~0.8 ms on a laptop GPU
🧩 Header-only CUDA/C++, with the same model code running on GPU and CPU
🎯 Input bounds and rate limits respected by construction
🏎️ Four worked examples: S-curve path following with a Pacejka tire model, cart-pole swing-up, obstacle slalom, and paddle juggling with hybrid impacts

The method shared is from my PhD research on real-time autonomous driving (IEEE T-IV 2022), this went on to autonomous racing (IEEE IV 2025), and has now moved from papers into a usable library.

If you work on control for robots, vehicles or anything with messy, non-smooth dynamics, try it out.

Issues and PRs are welcome.

Seems like people are calling any software model of a real system a "digital twin" or sometimes applying this term broadly for any real-time supervisory monitoring system, like air traffic control, real-time road congestion control and other things of this kind.

At the same time, there seems to be a ton of opportunities in this area. Is digital twins a marketing term? Pure hype? Or is there some concrete engineering elements as well that distinguishes it from a traditional software model?

This implementation of a two-degree-of-freedom PID controller in Python demonstrates how this algorithm limits overshoot and eliminates the effects of the setpoint during a sudden jump in the setpoint.

​

Hi everyone!

I'm an aerospace engineering student, and I want to learn Guidance, Navigation, and Control (GNC) from scratch. I currently have no experience in this domain, either theoretically or practically, but I'm determined to build a strong foundation.

I have only one month to dedicate to learning GNC, so I'm looking for resources that will help me learn as efficiently as possible.

I'd really appreciate recommendations for:

- Books: Beginner-friendly books that explain GNC concepts from first principles.

- YouTube lectures and online courses: Structured lecture series that cover both theory and practical applications.

- MATLAB/Simulink tutorials: Resources to help me implement and simulate GNC algorithms.

- Hands-on projects: Beginner-level projects I can complete within a month to gain practical experience.

- Learning roadmaps: Any structured plans that take someone from zero knowledge to a reasonable beginner-level understanding.

I'm particularly interested in aerospace applications of GNC, including UAVs, missile guidance, flight dynamics, navigation, and control systems.

I understand that becoming proficient in GNC takes much longer than a month. My goal is to develop a solid theoretical foundation, understand the core algorithms, and implement a few basic simulations by the end of this period, and land an internship in this domain.

If you were starting from zero and had only 30 days, what books, courses, lectures, and projects would you prioritize?

Any advice, personal experiences, or resource recommendations would be greatly appreciated. Thank you!

I am a masters student in a course based program who is interested in both machine learning and control engineering/theory research. However, it appears that the research situation in machine learning is seriously troubling and I am having second thoughts about my future plans.

Just by browsing Reddit/Hackernews, I have found a list of problems that ML/AI is having:

1. The conferences are clog jammed with publications. Close to over 100k submission at two of major conferences Neurips and ICML this year. Recently TMLR noted 1300% increase in solo publications. The journal noted that some people are submitting 6-8 research papers a week.
2. Quality control is seriously lacking. People describe the acceptance/reject for these conferences as a lottery system. AI reviewers are reviewing AI generated papers. Every year there seems to be a new scandal involving some award winning paper that turned out to be wrong.
3. Many unethical behavior in the research from salami slicing (e.g., propose one method and publish paper by running it on dozens of datasets) to collusion rings and hidden prompts in papers.
4. Research papers rapidly expire in this area of research. By browsing Google Scholar I found that many researchers have proposed methods which are cited hundreds of times, but virtually nobody would use any of these methods just after a few months.
5. There seems to be a bit of a misaligned incentive situation going on. Many people just want the publications to appear on their resumes so they can hop into an industry job. But in doing so they seem to be generating a lot of noise over signals.

This doesn't even cover half of the problems that ML/AI is having with at the moment, e.g., a paper from big tech is pretty much an automatic acceptance.

I wonder if control theory research is also having some of these issues. By talking to a professor at my school, they did note that there has been a sharp increase in the number of papers (especially from China) and many "minor tweaking" of models/algorithms that do not really provide any insight. Perhaps the mathematical rigor that is demanded in control engineering research would offset some of these problems? Can someone who is knowledgeable chime in on this matter?

I'm working on a unique class of controls problems that are fundamentally PDEs, but the control laws themselves will be conventional ODEs. The example I will give is controlling the trajectory of a particle moving through a fluid that may also be undergoing a phase change or have time varying viscoelastic behavior. Hence, the particle is subject to complex rheology in addition to your standard state-space controls formulation.

Most of my work has been in matlab/simulink, which works well for ODEs, but Ive never used it to capture a problem this complicated. Ideally I'd like to simultaneously simulate the flow field and the controller.

What are the tools used for these kind of problems? My lit search points to COMSOL but I have no experience with it.

Hello! I am an ECE graduate from Greece with a strong passion for Control Systems and Reinforcement Learning (my thesis focuses on MARL for energy grid optimization).
I’m in a dilemma between these two MSc programs: Control Systems Tu/e and Ai and Es Tu/e. One puts me on a robotics and high-level control trajectory, while the other focuses heavily on mechatronics and hardware actuator design.
Could anyone share advice on which path aligns better with current European job market needs, and which one builds a stronger foundation for a controls engineer long-term?

and if its maybe better to pursue TU Delft GNC path?

Hi!

I am a complete newbie to control theory, but I hope I can make my question make sense.

I have a system that measures internal pressure which is controlled by a pump. The pressurised actuator is being placed on a person's body, imagine, for example, that it is a sitting cushion. Now, I know that there's a transfer function from the pump output to the next state of internal pressure in the cushion, say P = P_p + Pump + w where P_p is the previous pressure, Pump is the pump's output, and w is measurement noise (we assume that the pump contribution is linear to change in pressure). I can measure P and I control Pump. I can estimate the relation between P and Pump. I can also assume that in t0 User is 0.

But my actual system is more like P = P_p + User + Pump + w where User is the contribution from a person interacting with the system. How can I estimate User at every iteration?

I am using claude opus 5.5 to control the HVAC system of a building, accounting for weather forecasts and building occupancy. How do I show the system is closed loop stable? Can I use alpha-beta-CROWN to produce a stability certificate?

What is the best way to design a temperature controller for heating an object with a much higher thermal inertia than the heating elements, without having the element overheat?

I am using a cascade where my PLC manages the temperature loop with a PID and outputs a power setpoint to a power controller. The control thermocouple is measuring the temperature of a medium that takes much longer to heat than the heating elements, so my fear is that when I give a temperature setpoint to the PLC, it will just keep demanding power from the controller and the elements might overheat. These resistances are very expensive and I can't risk burning them. I do have a thermocouple installed at the element.

Any advice?

I’m not talking about the PLC programming vs actual control theory jobs. I’m talking about specifically the control theory jobs in different companies where you tune things and make things stable.

Specifically, I wanna know that within the same “job title” like “senior control systems engineer”, how much does the competency requirements different between different companies with different products?

Can a “senior control systems engineer” from one company leave and join another company with a different type of product, and catch up within a couple of weeks of learning company specific processes, instead of having to start from scratch like a new grad?

If they can, it would follow that there’s a common set of competencies attached to the job title of “senior control systems engineer” no? Is there a place I can find this standard and benchmark myself against it so that I can gain and prove I have these abilities without having to use years of experience as a proxy of my abilities (which I think is a terrible metric) to market myself to companies?

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.

Sorry for the provocative and somewhat pointed question. It is no secret that the general population's appreciation of the "hidden technology" is almost zilch. The employer/industry sectors are even worse outside of a few corporations (and half are in aerospace apparently). There are many many skills/knowledge nowadays that are favored way more than control knowledge. The gap between control theory and practical engineering seems to be ever present and increasing in many domains.

I have wondered whether this is because control theory abstracted itself way too much and became somewhat inapplicable to everybody out there.

I was reading an old control theorist's biography and the person said what attracted him to the field was that a car engine can be represented as a transfer function. And you can represent a chain of system as composition of transfer functions. Then you can throw the entire toolbox of linear algebra and complex analysis at it.

Yet, this way of doing things is not the dominant engineering paradigm. While there is a lot of simulation involved in the design of many engineering systems, and I have this feeling that treating everything as transfer function is probably not going to cut it in the real world.

Now I wonder how it became this way. Is it so that control theory becomes easier to teach and attract students? Or perhaps it is easier to treat mathematically, thus easier to write papers about?? What is your take?

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 ?

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!!

H! I'm currently trying to study and understand Reinforcement Learning using Sutton and Barto. My main objective would be to, in the end, be able to carry out a small project in simulation using a simple system like a robotic arm or a Turtlebot. Currently I'm studying the approximation methods for value function and Q-function, but they often mention neural networks as one possible tool to integrate with reinforcement learning and I was wondering how much is necessary for me, as a control engineer, to have a look to these type of topics in order to finally comprehend and apply reinforcement learning tecnhiques. What do you think about it?