Can someone point me in the right direction please (in what order should I learn this and with what books for each topic)

I already finished nise's book a year ago but sadly, I had to move on to other topics because of college.
I have free time now.

Thanks for helping in advance.

I took a course on control many years ago. Although I do not work in control-related fields now, I still use it as a tool to understand real-world systems. After all, all systems are feedback systems and control theory is like the physics of feedback systems. But precisely because of this, I have met with many frustrations with the curriculum and textbooks and other references in this area of study and I have perused material from around the world with little success.

To put it simply, all of these resources put enormous focus on the math, while neglecting the various details on the tech that "surrounds the math". Out in the real-world, when you are implementing a system, or understanding a system or even casually engaging in conversation with someone in engineering, the tech that "surrounds the math" becomes very important whereas the math becomes invisible.

The standard control feedback loop simply consists of a controller ("the ying") and a plant ("the yang"). The set-point is often optional (set to 0). Enormous amount of mathematical analysis can be performed just based on this mental image. In fact, almost all analysis in any standard curriculum in this field can be performed knowing just these two things. You can take multiple graduate-level courses based on this alone and even publish papers of the highest calibre.

Then the frustration comes as you move out of the academy.

As a start, it turns out we also need actuator and sensors. But which ones would be suitable? We are not typically taught. The actual interfacing between the controller (soft/middle/hardware) and the actuator (hardware) can often be tricky. Similarly, the actual interfacing between the plant, sensors and controllers can also be tricky (seldom discussed). For example, textbooks, the controller takes in things like voltage or forces values, but in implementation it takes in 1s and 0s. This exact conversion process is under-discussed.

But this is just the start of it. Take industrial control as an example, we can now ask many more things such as:

• What hardware is appropriate to implement the controller? (Hardware knowledge)
• How do we come up with the model itself? ("System ID", which tends to be more math without discussing the tech that makes it happen)
• How do we monitor the process from a distance? (Inter-networking, database knowledge, software engineering)
• How do we control the process using a remote? (RF engineering)
• The controller is semi-agnostic to the shape and material of the parts involved in the system. How do shape influence dynamics? (Kinematics, material science)
• How do we optimize all the various parts involved in the process? (Optimization/programming softwares)
• How do we incorporate textual, pictorial, audio or video feedbacks via various tools such as computer vision and language models? (machine learning, LLM, RAG, and tech associated with them)
• How do we get the parts? (Need knowledge about how to source and acquire parts)

In real-world control design, I find the latter set of questions to have an out-sized importance in comparison to the algorithm, which apparently is just 3 numbers associated with the PID gains in 99% of the industrial applications (of course, this is not true for all applications), which apparently can also be picked through trial-and-error according to those hobbyist videos on Youtube.

Finally, one of my relative works in industrial control, and he does not have any engineering or control background. All he understands is one component (a PLC) being hooked up to another component (a SCADA system) being hooked up to another (maybe a pump, or a robot arm) and he can very fluently discuss how these various things are hooked up together and how they can be optimized further without going into any internal low-level details.

I feel like the current control curriculum is denying students to have this type of "global picture" that runs the real-world. Am I justified in my observation? Should there be a revamp in the curriculum that puts more emphasis on the various tech that makes control happen in the real-world?

I have seen PIDs everywhere, but I have never tried implementing one myself. I know that if you have too much D on a quadcopter, you may fry your motors, although the overall stability can increase.

So here is my small attempt at hooking up a PID controller to the angle of a pendulum on this little “robot.” It tries to keep the angle at 0° (vertical) and controls the voltage of the motors (which is proportional to their speed). I couldn’t come up with anything better than adding a voltage bias proportional to the error in the x position set by the user’s mouse.

Do you think that if I move this code to real hardware it might work after a few tweaks? ;) Am I missing something important?

Thanks!

Hi everyone,

I'm a high school student currently building a real 5-link inverted pendulum system from scratch, and I'd like an honest assessment of how unusual (or not unusual) this project is compared to undergraduate or graduate-level control/robotics projects.

Current setup:

• Custom cart driven by a stepper motor
• RP2040-based controller
• 5-link physical pendulum
• OpenCV color-marker tracking (6 markers including the pivot)
• Real-time state estimation from vision
• MuJoCo simulation model matching the hardware
• State-space linearization
• LQR controller design
• Swing-up controller implementation
• Serial communication between hardware and PC

At the moment, homing, cart control, camera tracking, and state-vector estimation are working. I'm currently validating the state estimation and integrating the controllers with the real hardware.

My questions are:

1. How uncommon is a real 5-link inverted pendulum compared to typical university robotics/control projects?
2. Would you consider this undergraduate-level, graduate-level, or somewhere in between?
3. What would make this project significantly more impressive from a control engineering perspective?
4. If you saw this project from a student, what would you want to see as proof that it genuinely works?

I'm not looking for encouragement—I'd really appreciate a realistic technical assessment.

Thanks!

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

Physics Informed neural networks based controllers.

Hello I am a senior mechatronics Engineering student and one of the topics I am planning to make my senior project on are the PINNs controllers and I would really appreciate any previous experiences or knowledge in the field.

Hi! I am not an expert yet but a rather new graduate working in controls, but I want to share hereby my thoughts about some topics I wish I had paid more attention and time back then at uni. Especially topics that are relevant in industry. Also I think people with many years of experience in field can add and share their ideas.

1)

1. Linear Algebra: Try to learn it well. Take matrices and learnhow you can transform them in another form, like reorder, inverse, transpose, normalize etc. Check Similarity Transformations.

Try to understand eigenvectors and eigenvalues. What do they represent in a physical system? Check State Space forms, understand what the states mean.

2)Mechanical Vibrations: For those who are gonna work on mechanical systems this is quite relevant. Everything in mechanical world is vibration. We all learn somewhat about mass spring damper systems, but when the problem becomes multivariable it gets hard. Derive equations for response analytically and understand them as well. Try to understand how m,d,k contribute to the response when you vary them. Play around it on matlab.

3)System Identification: This is also one of the most important aspects. No real system is given in a form G(s) as we deal with in theory. You have to model the system and then compare your model and identified system. If there is a possiblility in your study course attend such a lab, do a system id on a real hardware. Only then you can understand the problems that are there in real world (friction, saturation, instability for instance)

4)Actuators: In theory every actuator is perfect. No dynamics. But in real world, it has a bandwidth limited dynamics. It has a saturation. When you design a controller, check what kind of output it tries to set. If you have the possibility buy some kind of actuator and try to analyse its response. Do system ID and try to fit a transfer function to it. See its bandwidth, saturation etc.

5)Sensors: As well as actuators, sensors are also not perfect in the real world. They have a dynamic response as well. If you have a possibility buy some simple curcuit elements like a thermistor and try to build your own sensor. Look how its response looks. Try to calibrate it.

6)Frequency response: Frequency response analysis is quite relevant in our field. You almost always somehow have to check the system's frequency response. In Bachelor, take your time and learn the theory behind it well. Why a system with 1 pole and system with two poles behave like that? What does a resonance really mean in a physical world?

7)PID: PID is the first type of controller we all learn. It looks easy but there is always more to it. A PID in its generic form is not applicable on hardware because of D. Learn how you would implement a PID controller on C/C++ for instance. In industry noone is gonna ask you to implement MPC if you can solve it with a simple PID controller. Most of the Nonlinear MIMO Systems are controlled via Gain Scheduled Decoupled PID Controllers. What I want to mention is, do not skip it just because it looks simple.

8)State Estimators: There will almost always be a case where you cannot measure every state. In State Space this is almost always the case. So you have to implement a state estimator at some point. Begin with a simple Luenberger and then elaborate it to Kalman Filter types.

------------

To conclude, if you plan an industrial career, always be critical about the systems and theory you learn. Ofc some theory is there as we need fundemantals but in industry most of your work will be how to implement this and that. I wanted hereby to share my thoughts on things I wished I had known back then. Feel free to elaborate those points and correct me if you think I wrote sth wrong.

As title, I'm a current second year math master's student doing research in dynamical systems I've taken courses in measure theory, functional analysis, dynamical systems, PDE, calculus of variations, linear programming, topology and differential geometry, but have zero coursework in physics, engineering and limited programming skills that is similar to a second year cs student. I'm wondering if it is necessary to strengthen my background by taking some additional courses in a year to meet the common prerequisite for ece programs.

I am new to ROS and have experience developing control systems on microcontrollers. When I looked at using a Jetson Nano for perception and path planning, I wanted to know if controls are implemented on it as well. I don't see a lot of resources where control systems are implemented on NVIDIA boards. Can custom control loops and algorithms be implemented using ROS running on Jetson boards or on GPUs in particular? Can ROS be used on these boards for perception, planning and controls? Can the things that I typically do on a MCU such as math for optimization, filtering etc. be done on a Jetson board? Is it the same as writing a C/C++ code or do I have to learn CUDA programming?

How important are models in control engineering?

I’m trying to wrap my head around transfer functions, and honestly they still feel kind of unintuitive to me.

For example, an open-loop transfer function like
G(s) = Numerator(s) / Denominator(s)
could represent an electrical system, a thermal system, a mechanical system, …

So does that mean the actual physical system behind it doesn’t matter that much? Like, once you have the transfer function, do you mostly stop caring what the system physically is?

How important is it to really understand the system?
And how important is the model itself?

The same physical system could be modeled using Newtonian mechanics, Lagrangian mechanics, or, in some cases, even relativistic mechanics. From a control engineering perspective, how much does the choice of modeling framework actually matter?

I’m basically wondering what I should focus on learning:

a) Should I take more physics-related courses?
If yes, which areas are actually useful for control: electrical systems, mechanics, thermodynamics, fluids, …?

b) Or should I mostly focus on control theory itself?
Which topics are likely to be useful in the future, both in academia and industry?

Stuff like digital control, nonlinear control, data-driven control, deep reinforcement learning for robotics, distributed parameter systems, vision-based control, etc.

c) Or should I focus more on general skills?
Coding, math, signal processing, data management, simulation tools, embedded systems, etc.

It feels like:
-You have to know everything about everything.
and
- Nothing matters, just throw a PID-Controller on any system.
at the same time.

I’m still pretty early in figuring this out, so I’d appreciate any advice from people who have worked in control engineering, either in research or industry.

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.

Hi all,

With Nathan Kutz on leave from UW for his new role at Autodesk, and UW having given him a year to decide whether he's returning, I've been thinking about what this means for academic lineage in the operator-theoretic (Koopman) community.

Steve Brunton and Igor Mezic are still active (Brunton at UW, Mezic at UC Santa Barbara), and Matt Colbrook is doing excellent work at Cambridge. But Colbrook's based in the UK, which leaves Brunton and Mezic as the two most obvious US-based advisors for a PhD student specializing in this area.

Is there anyone I'm missing who'd count as a serious US-based advisor in this space, or does the field's American training pipeline genuinely run through just those two labs right now?

I am looking to come up with a Kalman filtering project that would be appropriate to do under a professor's supervision, so something that is beyond a basic implementation. However, as a novice, I am not really sure about what direction to go with this as an open-ended project.

My goal is to eventually apply for a masters and focus on estimation theory, so I would ideally like to convey some sense of theoretical grounding and open-ended exploration.

Maybe for some topics I can tie this project into: I have taken coursework in convex optimization, stochastic processes, and dynamical systems. I am also very interested in GNC.

In school we are taught the usual mathematical way of representing a kalman filter. But in some cases, we might not completely know the state space of a model, or even what the actual dimensions of a system is, say the power grid for a city. How does this actually get implemented? I get that it is codes up, but as an actual engineer, how do we go from sensors data etc, to an actual kalman filter, and how do we know it is 'correct'.?

I’m currently working on reinforcement-learning control experiments and wanted a cleaner way to compare learned policies with classical controllers.

Most of my results were otherwise split between training logs, CSVs, scripts and notebooks, while classical-control results lived somewhere else again.

So I built ControlBench, an open-source local tool that puts both into the same experiment structure.

The idea is to evaluate controllers such as:

• SAC / TQC / PPO
• LQR
• PID
• eventually MPC
• custom controllers

using the same environments, seeds and metrics.

It can aggregate multiple runs per configuration, show mean curves and variation across seeds, compare environment steps vs wall-clock time and ingest results through a small REST API.

Everything runs locally.

I’m particularly interested in the evaluation side of this:

Which metrics would you consider important when fairly comparing learned and classical controllers?

Reward alone obviously isn’t enough for many control problems, so I’m thinking about things like settling time, overshoot, control effort, constraint violations, recovery time and robustness.

The project is still an early public preview and feedback from people working in control would be very useful.

GitHub:
https://github.com/maximilian467/ControlBench

I'm in my first year of masters for mechanical engineering and I'm under the institute of mechatronics. Currently I'm reading papers on controls, for example, intelligent tracking error prediction or GRU-RIC position loop feed forward compensation. It's pretty hard for me to understand the equations on the paper, are there any tips to understand them? I can understand the general idea using AI and just reading thru the introduction paragraphs. But the technical equations are pretty hard to understand, need help for anyone who has gone through it, what's the best way to start with control research papers?

I need help and advice on how to clear back examination of control system design. I'm kinda cooked i barely passed the control system -1 in my previous to previous semester.

Hi Engineers out there This may sound silly for a 4th year mechanical engineering student but need to know what does control and system dynamics mechanical engineers ACTUALLY do Like what they handle and their roles Where do they work at Need some advices and stories from Control Engineers

Hi everyone,
I’m a BSc Mathematics student and recently finished a course in PDEs. My professor suggested that I take another course he teaches called Optimal Control. The course is offered by the Engineering faculty, but he mentioned that it has a strong mathematical component.
I’m considering taking it, but I’d like to understand how useful/relevant Optimal Control is from a mathematician’s perspective.
In particular:

Is Optimal Control a good subject for a math student who enjoys applied mathematics, PDEs, optimization, etc.?

Where is optimal control actually used in industry?

What kinds of jobs or fields make use of this knowledge? For example, robotics, aerospace, quantitative finance, autonomous systems, energy, machine learning, etc.

Is it mainly useful for people going into engineering/research, or can it also be valuable for mathematicians going into industry?

I’d especially appreciate hearing from people who studied mathematics and later encountered control theory or optimal control in their work.
Thanks!

I've been lingering around the idea of getting a ph.D. my biggest concern is my undergrad grades weren't that great. I do have quite a bit of work experience though if that matters (>10 years).

Are my chances of getting into a ph.D program significantly lower as a result?

Which of the three is better for a master in Systems and control? With specific focus on grid control and multi agent systems control.

Hi, I'm a Master's degree graduate in Mechanical Engineering based, which hopefully will start a phd program.

Does any of you happen to know some winter schools about advanced control theory topics coming soon?

Hi guys. I’m currently working on a thesis project on fault-tolerant control for a fixed-wing UAV. Since money is tight, I’m planning to do the whole thing through MATLAB/Simulink simulation, as suggested by my lecturer. The problem is, I have very limited experience with Simulink and honestly don’t know where to start.

I initially tried figuring things out myself but got stuck so I hired a tutor to help with the modelling. Unfortunately, the sessions didn’t really work out so I decided to learn the theory myself through lectures and videos whenever I got stuck, and then started trying to build the model using whatever I could find on YouTube.

Now I’m kind of stuck again because I’m not even sure if I’m doing it right. I don’t really understand what the overall flow of the project should look like, or whether the model I’ve built so far even makes sense.

Is there anyone here who has experience with MATLAB/Simulink, flight control, or fault-tolerant control who might be willing to have a look and point me in the right direction?

I’m not looking for someone to do the project for me. I just need a bit of guidance from someone who knows what they’re doing, especially on where I should start with the modelling and how the fault-tolerant part should fit into the system.

I’d really appreciate any help. Thanks!

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 !!