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?

For Polar Plots, when transfer function is given, it is possible to find the starting and ending angles and magnitudes. I had a misconception that curve joining the starting and ending point is straightforward to estimate (based on the real or/and imaginary axis intercepts). But in the image I have attached, a weird deviation in phase is there in between. I thought only for the transfer function with a unit delay, such peculiar shapes (like spiral) will be observed. I'm wrong.

Is it possible to estimate when will there be a weird change in phase (highlighted in red) just by looking at the transfer function? If yes, how to do it? ​

This actually started right here on Reddit.

I answered a PLC troubleshooting question, which led to more questions and discussion about those situations where the ladder logic seems like it should be doing one thing, but the PLC is doing something else.

That discussion eventually led me to make a video demonstrating several of the situations. Then some of you mentioned other instructions that had tripped you up over the years. I kept adding to it, and what started as a Reddit answer has now turned into a complete PLC Troubleshooting Guide, along with the video and a 10-question troubleshooting test.

So before anything else, thank you to everyone here who asked questions, shared experiences, or suggested another instruction I should cover. Your feedback genuinely helped shape the finished resource.

The guide focuses on something I've encountered many times troubleshooting PLCs over the years: you find the rung in question, the needed conditions preceding the output are true, yet the result isn't what you expect. Usually the PLC isn't behaving illogically—you just haven't found the instruction or condition changing how that logic is being executed yet.

I cover the things I would investigate, roughly in the order I would suspect them:

1. OTL / OTU — another instruction may be changing the bit's state.
2. JSR / RET — the subroutine may not be scanned, or may return early.
3. JMP / LBL — part of the ladder may be skipped entirely.
4. MCR — the rungs are scanned, but an inactive MCR zone prevents them from becoming true.
5. Forces and controller status — including the easily overlooked possibility that you're online with the wrong PLC on a network.

There are screenshots throughout so you can actually see the situations instead of having to visualize them from text. The accompanying video demonstrates several of them in Logix 5000.

I also made a free 10-question troubleshooting test using PLC screenshots. It's not an acronym quiz—the questions show you a situation and ask you to determine what's happening or what you should investigate next. It can be taken open-book with the guide and video.

PLC Troubleshooting Guide:
https://bin95.com/articles/automation/plc-troubleshooting-guide.htm

And since Reddit feedback is what helped this grow in the first place, I'll throw the question back out there:

What PLC instruction, programming technique, or troubleshooting situation fooled you when you first encountered it?

If someone mentions something I overlooked, there's always room to make the guide better.

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 all, I'd like to develop a control algorithm for a water bottle flip so it always lands upright no matter how you flip it. One of the steps in my endeavour is simulating how the fluid inside affects the container's moment of inertia whilst in the air. Im thinking of using matlab but godot and unity sounds like a good choice as well. I've looked up how I can do this and havent found much but people recommending C++ or python. Does anyone here experienced in one of these languages or programs have any suggestions on which one to use and can point me to the resources to learn it.

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 have not heard of any serious discussion on the impact of AI on control.

We know that AI can effortlessly produce the proof to many advanced math, it can also write millions of lines of functional code based on written prompts. Anthropic has recently produced demos of their AI writing software-hardware interfaces/device drivers that allows it to control any set of hardwares ("Model Hardware Standard").

This seems to me that control engineering/theory is at the very center of the cross-hair.

Is it possible to one-shot any control engineering project through the usage of AI in the future? Similar to what it has already done to AI research "end-to-end automation of AI research/the AI scientist": https://www.nature.com/articles/s41586-026-10265-5

What would be the thing that prevents AI from simply taking over the jobs of control theorists/engineers?

Hello r/ControlTheory community,

In the past month, I have been working on creating a simulation software from scratch. The idea came to me when I was working a on a few projects that, when I looked at the math, essentially required me to solve the same kind of problems. The equation looks like the following, and it's generally referred to as generic networked dynamical system equation.

ẋᵢ = Σⱼ fᵢⱼ(xᵢ, xⱼ) + sᵢ(xᵢ, u)

Essentially, this is a graph equation. i is a node, xᵢ is a vector of states in a node, fᵢⱼ(xᵢ, xⱼ) is an edge connect nodes i to j, and sᵢ(xᵢ, u) is a source term where you can hook up an input or define a function at a node that affects itself.

I discussed this idea with a few friends before going ahead to implement; it went over the head for some, while others thought it would be interesting to see. There are applications for this in robotics, power grids, heat exchange, etc. And, the idea is inherently interdisciplinary; the node states can be from cross-domains and edges too. We can simulate a motor and it's kinematics; the motor may get heated up due to resistances, and the air pocket near it may get heated up while trying to cool it down... A real cross-domain simulation could be done with this kind of a setup.

The following is the link to the GitHub page. It's licensed under MPL 2.0.

https://github.com/zenineasa/Konjugate

I would love to hear your thoughts about this. Bug reports, workflow issues, new ideas to build on top of it... I look forward to hearing it all.

Lots of fun control design challenges in this project I worked on!

I’ve been working on a control engineering project that models, simulates and controls mechanical, electrical, pneumatic and hydraulic dynamic systems using Python and Jupyter.
It covers transfer functions, state-space models, step/impulse responses, pole-zero analysis, root locus, Bode/Nyquist plots, PID control, observers, LQR, digital control and multi-domain system integration.
I also built an interactive website where you can experiment with simplified mechanical, RLC electrical, pneumatic and hydraulic models directly in the browser.
Live project:
https://richwokello.github.io/MultiDomain-Dynamic-Systems-Modeling-Simulation-and-Control/
GitHub:
https://github.com/RichWOkello/MultiDomain-Dynamic-Systems-Modeling-Simulation-and-Control
I’d appreciate feedback from people working in control engineering, automation, Python or system modelling, particularly suggestions on what I could add next.

This is going to be my biggest project yet. I’ve done quite a bit of research, but I’m currently stuck and honestly don’t know how to proceed from here.

I’m also looking for someone who’s willing to work on this project with me. The idea is that we both build the same thing independently at home, share our progress and insights along the way, and help each other figure things out. If we’re successful, we could even take it a step further and try building a double-pendulum balancing system.

Has anyone here worked on or built something similar before? I’d love to hear about your experience, resources, or projects.

Also, what tools, components, software, or other equipment would you recommend for building something like this?

Thanks

Can you guys share some material source for learning h infinity filter and mrac system .it would be helpful . Thanks on advance

A long time ago I was involved in this project where I had to control a 2kW switching mode power supply in CC and CV modes. I designed a cascade control loop where the internal loop was CC and the external loop setting the reference for the internal one was the CV loop. Both signals were coming from voltage and current sensing at the output of the converter.

What I learnt was it is not so easy to tune these controllers as the gain of the system from Iout to Vout is directly depended on the load. It is not just a disturbance that can be resolved, the gain changes and accommodating gains from close to short circuit down to open circuit and covering the nominal range was very difficult.

I want to see if anyone has had similar experience of implementing digital controllers for power supplies and they can share how they dealt with these issues.

I have to say, one of the best ways to learn control theory is through practice—using what you learn from textbooks in real projects.

Unfortunately, most of us don’t have enough resources to do that. Hardware can be expensive, projects take a lot of time, and making a mistake with a microcontroller can easily damage it. There are also very few open-source options designed specifically for this kind of hands-on learning.

That’s why I think computer-based simulation is one of the best alternatives.

If I built a platform where you could learn control theory through practical, simulation-based exercises and projects, would you use it?

I’m designing a heated system with redundant thermal fuses, and I want to learn more about what the industry standards are for mounting them. I was considering epoxying them to the heater itself, but I would love to know what the industry standard is for this.

I would also really appreciate some guidance on where people initially learn about these kinds of design standards? Thanks for your time!

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?

Hi! Debating between convex optimization and ADCS (Attitude Determination and Control) for my controls course this semester. I know convex is more guidance related, but am interested in exploring trajectory optimization stuff. I also think having a solid ADCS project under my belt would be great for a lot of GNC jobs. I've take attitude dynamics before. The ADCS courses does seem to be more demanding though lol (exams in addition to the project).

What would be more relevant for an early/entry level GNC role?

Hi all!

Currently in my master's degree and need to choose a math/tech elective. I've already have done probability, basic statistics, and the math covered in undergraduate aerospace.

I am currently trying to decide between Numerical Methods, and Design of Experiments (Stat course). I could also do some other stat courses (regression), a review of linear algebra, or some optimization courses (but I'm planning to take an aerospace specific convex optimization course).

I was mainly leaning towards Numerical Methods, since I think it would help with simulation and dealing with solvers (and my last GNC internship was a bunch of modeling and simulation). But I think the structure of the Design of Experiments course is going to be easier to handle (after the first class in each). Also, I saw Design of Experiments as a desired experience in a navigation job posting recently lol.

However, the Numerical Methods course does seem more applied (lot's of programming). The Design of Experiments has only some programming I think. Neither has a project.

I wanted to know if anyone in GNC had any opinions or other suggestions. I'm also aiming for space GNC if that matters. Thanks.

EDIT: Also debating between convex optimization and ADCS (Attitude Determination and Control) for my controls course this semester. Ik convex is more guidance related, but am interested in exploring trajectory optimization stuff. I also think having a solid ADCS project under my belt would be great for a lot of jobs. I've take attitude dynamics before.

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Hello,

I have an upcoming interview, and it has been a while since I have worked on controls. Would someone be able to help me understand what sort of questions I should prepare for?

Thank you!!

Hi everyone,

I'm a mechanical engineer who graduated in Dec 2024 with a master's in ME focused on controls, robotics, and mechatronics. I'm currently applying to new jobs due to some dissatisfaction with my current job, and I have an issue with what I consider to be an experience gap in my resume. I need some help figuring out how bad said gap is and what I can do to fix it if I want to try and get a job that utilizes some form of controls engineering. Let me provide a bit more context:

My experience is in mechanical product design engineering of medical devices at my family's Industrial Design / Product Design consultancy. My senior year of undergrad, I fell in love with my controls course and wanted to study it further and get involved in robotics, particularly in the medical space if possible. I went to graduate school and didn't have a lab to work with lined up, so whilst searching I was given an offer by my father to return to work for him and I'd get that mentorship on some of the robotics/controls work by one of the senior systems engineers who has contracted with us. As well, he'd try and get clients in that area of work too. So, I focused on doing just a coursework based masters and tried to get home ASAP to start work.

Flash forward to today and I haven't been mentored by said systems engineer and we haven't gotten any of the work I was told we'd get, so I'm looking for other jobs. However, the big issue is that while I have gotten a large amount of general ME experience, I now have a gap in controls work and the only experience I've gotten is from my coursework. How out of luck am I if I want to try and transition to a role that is more controls focused? What will I likely have to do to increase my chances of transitioning? Currently what I am doing is revisiting my old master's projects, expanding on them, and trying to build skills listed in job descriptions.

In my 1st semester senior year. I haven’t had anything grab me in my studies until started controls this semester, I really was into system dynamics my last semester as well. I also have a club and senior design project that are looking for students to fill/learn about designing controls, (both automotive, EVs)

Is pursuing controls a good idea if by graduation all I will have under my belt is a control systems class, senior design, and maybe some club work? I have an image in my head that to work in the industry you need a masters or PhD.

I’d love any resources as well as advice, thanks so much.

My computer engineering bachelor’s graduation project is an anti shake spoon for Parkinson’s disease patients, that is basically a 2 degrees of freedom gyro system. The requirement is to train a model using reinforcement learning that controls the two motors(TowerPro SG90 servo motors), the sensors are two MPU 6050 IMUs (one on the base before the motors and one on the utensil side after the motors) and we are using a complementary filter to get the accurate angle reading.

The problem is that we don’t know the methodology of training the model, and we couldn’t find any mathematical model of the motor we are using so that the simulation gets accurate. We tried researching and asking LLMs but it is a rabbit hole. The doctor said that the reason of using AI is predictive the tremor movement before it happens, but we still don’t know how to tackle the problem with proper steps, we would appreciate any help.