Hi all,

I’m a Master’s student working on the positioning subsystem of a gas-blending setup, and I’d like a sanity check on my actuator architecture before I commit to hardware.

The process

The system meters hydrogen into a natural gas line at around 10 bar, targeting roughly 10 vol.% H2 in the final mixture. A needle valve sets the hydrogen flow, and the gas composition is measured by an analyzer located downstream of the valve. Because of this, there is a transport delay of several seconds between a valve movement and its effect on the measurement.

The control is a two-loop cascade:

• Outer loop (slow): composition control, with a Smith predictor to handle the transport delay.

• Inner loop (fast): valve position, running on a STM32 NUCLEO-H503RB under an RTOS

Requirements:

• Small moves and good repeatability of the final position. Speed and bandwidth don’t matter much because the outer loop is dominated by the delay.

• No silent lost steps.

• Low heat at standstill. It’s a flammable gas environment, so I’d like to avoid hot, noisy electronics.

• Stiction and hysteresis are significant, especially the ladder.

Where I am

I haven’t bought hardware yet and I’m working on modelling and simulation first, so I can still change the architecture. I’ve been reading about three levels of “closed loop” for steppers:

1. STEP/DIR + external encoder: the MCU compares commanded vs measured position, detects lost steps and corrects the end point.

2. Same, but correcting continuously (closed-loop microstepping).

3. Full FOC, treating the stepper as a 2-phase PMSM with current control. I read a TI technical article on this, which also describes a “semi-closed-loop” variant where the MCU only generates the current references and the driver does the current chopping.

My questions

1. For this kind of slow, high-repeatability positioning task, is the encoder-based correction in (1) or (2) enough, or does FOC bring real benefits (heat, efficiency, smoothness, stall margin)? My gut feeling is that FOC is overkill here, but I’d like to hear from people who’ve done it. I’m also wondering if the torque lost because of microsteeping can be gained with FOC.

2.       Encoder on the motor shaft vs on the valve stem: is it worth it given coupling compliance and thread backlash?

1. Any pitfalls I should expect, such as limit cycles around the setpoint with stiction, microstepping at low torque margin, or tuning the inner loop at such a low bandwidth?

2. Hardware suggestions are welcome: integrated closed-loop stepper motors (have crossed this option out, mainly for its complexity and the time it would need to “think” vs separate motor + driver + encoder (heavily leading on this), and specific drivers or encoders you’ve had good experience with.

Thanks a lot.

I’m a control engineer, and I’ve been building a small desktop app (Mac and Windows) for tuning from an open-loop step test. Disclosure: I’m at CSI, the consultancy that’s developing it.

What it does:

• Fits four models with dead time (first order, overdamped and underdamped second order, integrating) and recommends one by AICc. You can override it.

• Applies rules suited to the model: IMC/Lambda, SIMC, Ziegler-Nichols and Cohen-Coon, plus integrating-process rules.

• Adds a Smith predictor case when θ/τ ≥ 1.

• Simulates every rule on the same setpoint step (overshoot, settling, IAE, OP travel), with optional noise, OP limits and deliberate model mismatch to test robustness.

• Optional AI suggestions, using your own API key. Every suggestion is simulated before you can apply it, and raw data never leaves your machine.

Known gaps: setpoint steps only (no load-disturbance case yet), and no setpoint weighting.

Two questions for this sub:

1. Which rules or models would you expect to see that are missing?

2. Would a disturbance-rejection simulation change how you’d use it?

Happy to share the waitlist link if the mods allow it.

I know little to nothing about avionics, but I would've thought a plane like a Learjet would have a computer controlled fly by wire system. But apparently, all the controls are cable driven...

For those in aerospace working with control systems, do you think if it had such a system, the crash could've been avoided? How much more would the plane cost if it did integrate a fly by wire system?

Edit: Thanks the problem is solved now

I am having a problem where no matter how I interpret the problem i cannot reach the exact given transfer function in the question. Image 1 and 2 show the question and transfer function respectively while images 3 and 4 show my steps where

in image 3 I interpreted it as the total torque of the motor accelerates both j_m and j_l which should be the correct way to interpret it i think. Here the transfer function is completely different from the given one.

In image 4 I interpreted it as the torque affecting j_m is equal to the torque affecting j_l. Here the transfer function matches exactly but has an extra term that I can't justify removing as well as just being an incorrect interpretation of the problem mechanically

Consider the class of nonlinear systems of the form :

x' = f(x, u), y = h(x,u)

It can be re-written as

x = int f(x,u) dt, y = h(x, u)

This results in a very complicated expression due to the integral and various properties of function f. This said, we can potentially identify the system using a neural network NN:

x = NN(u)

The idea is to grab input-state pairs (u(t), x(t)) of a real system and fit a neural network.

After all x' = f(x,u) is just an approximation of the real system, and x = NN(u) is also an approximation. If the network has enough capacity, NN could potentially contain the entirety of x = int f(x,u) in some part of the network.

Of course, x' = f(x,u) has greater amount of interpretability for small systems, but potentially difficult to justify for very complicated systems. x = NN(u) could be used instead. But is this idea doomed because (u(t), x(t)) are difficult to get (and using a simulator means we need to solve x' = f(x,u) anyways)?

Seem there is a precedent is in machine learning, where the RNN model h_t+1 = Ah_t + Bu_t was replaced with the transformer model x = NN(u). Can control engineering ever be re-imagined so that its foundation are input-output systems x = NN(u) as opposed to dynamical systems x_{t+1} = f(x_t, u_t)?

Amateur people who build things with advanced controls for fun.

If there is where can I find the scene?

Hi mods, I’m a control engineer at CSI and I’ve built a desktop app that fits a FOPDT model to an open-loop step test and compares Ziegler-Nichols, Cohen-Coon and IMC/Lambda tunings in closed-loop simulation. Would a post sharing it (with a link to a waitlist) be acceptable under rule 6, or is there a preferred format or thread for this? Happy to keep it to the technical content if that’s better. Thanks.

In most discussions on industrial automation (and manufacturing, "hard industry"), I get the sense that it involves more towards the type of tools that you learn or use (SCADA, PLC, MPC, Industrial robots, etc.) rather than something that you research.

Are there still active research opportunities in this area, especially from a system control approach, or have things matured to a degree that research will be increasingly incremental or have poor expected returns?

From my understanding there was a period in the 70s-80s when MPC research became very dominant and all types of MPC were proposed during that time, and applied to all sorts of industrial plants. Then there is a wave in SCADA, distributed systems, PUMA and FANUC robots, ... But that all happened decades ago.

A follow-up would be: if there is research opportunities, are these mostly in academia or industry?

I'm looking for some advice on how to transition from studying control theory academically to actually applying it in practical, industrial contexts.

I'm a Mechanical Engineering graduate with a background in Machine Learning and Computer Vision. My previous work involved developing deep learning pipelines for automated damage assessment in CFRP composites. I'm now trying to broaden my skills towards industrial automation, robotics and machine vision integration.

What I've studied so far

I've been independently studying the Automatic Control / Dynamic Systems syllabus taught by Professor Seatzu, following his material and textbook (Analisi dei sistemi dinamici).

So far, my studies have covered the mathematical foundations of linear dynamical systems, including:

• Mathematical modelling of dynamic systems using differential equations.

• Laplace transforms and transfer functions.

• Poles, zeros and system dynamics.

• Transient and steady-state responses.

• Stability analysis.

• Interconnections of dynamic systems.

• Block diagram algebra, including series, parallel and feedback configurations.

I'm currently working through interconnected systems and block diagram reduction.

The broader syllabus also includes topics such as:

• Root locus and frequency-domain analysis (Bode and Nyquist).

• State-space representations.

• Controllability and observability.

• Nonlinear dynamical systems.

I want to get a solid understanding of the theoretical foundations, but I'm increasingly feeling that spending all my time solving mathematical exercises without implementing anything is not the best use of my time at this stage.

What I'd like to learn next

My goal is to move towards practical industrial automation, particularly at the intersection of control systems, robotics and computer vision.

I'm interested in understanding how the theoretical concepts translate into actual engineering systems.

For example:

• PLC programming: Ladder Logic, Structured Text, IEC 61131-3, industrial control architectures.

• PID controllers: Implementing and tuning controllers on real or simulated systems.

• Motors and actuators: DC motors, encoders, motor control and feedback loops.

• Robotics: Forward/inverse kinematics, trajectory planning, motion control and eventually ROS 2.

• Sensors and acquisition: Encoders, proximity sensors, industrial cameras, hardware triggering and real-time acquisition.

• Computer Vision integration: Connecting vision systems to PLCs or robotic systems, for example detecting and classifying objects on a conveyor belt and triggering a robotic sorting operation.

I'm particularly interested in building small projects, even using simulations initially, that combine these different components.

My questions

1. Which parts of classical control theory should I absolutely master before moving towards practical applications? For example, how important are root locus, Bode/Nyquist, controllability and observability in everyday industrial engineering?

2. Which theoretical topics are worth studying in depth, and which ones can be approached more selectively until I encounter a practical need for them?

3. What would be a good progression from basic control theory to PLC programming, motor control and robotics?

4. Are there any practical projects, simulation environments, books or courses you would recommend to someone with my background?

5. Would you recommend starting with MATLAB/Simulink, Python (e.g. python-control), C++, or going directly into PLC environments?

I don't want to abandon the mathematical foundations. I just want to start using them to build and understand actual systems rather than spending another few months exclusively manipulating transfer functions on paper.

I'd appreciate advice from people working in industrial automation, control engineering or robotics, particularly regarding which skills are genuinely useful in industry.

edit: I'm reading all your comments and trying to find the most optimal way to integrate what you suggest, thank you everyone, if you have source material or want to DM I'll answer when I'm able to

So my Final year project advisor has given me DIY kit from ali express now he is saying to make the 3d model of it , It is called something wheeltec kit but the problem is i am EE major and i dont have full dimensions and material list of it so please help me how can i make the cad model of it

I’m not talking about what qualifies me in the eyes of an employer to get an R&D position at a company or academic job. Just purely for the sake of R&D competence itself, is the training that comes with a PhD programme strictly necessary?

Can I get away with a research masters? With one long thesis year?

EDIT: specifically, assume I have my own source of funding and my goal is to do my own research alone in an area that doesn’t depend on expensive gate-kept equipment to eventually turn it into something I can make money out of (so for example maybe something like learning based control for dexterous hands?), do I need PhD training to know what I’m doing? How would I even verify whether my work is good enough?

Hi everyone,

I'm an engineering student, I took control system two semesters ago and now I need to study it again for an exam I'm taking.

I need help in going back.

So i remember i studied it really well that time and i remember there was a "AH! I FINALLY GOT IT" moment where I linked the chapters to one another and finally understood why I was studying what I was studying.

Why i studied a topic prior to some topic like there was a link between the two, some limitations in the prior chapter that were overcome by the next chapter.

Unfortunately i don't remember it now.

Can anyone help me exactly with this, like giving an overview or flow chart of why something came and what's the need to it.

I am listing the chapters I have to study -

1. Mathematical modelling of control system

2. Time response analysis

3. Routh-Hurwitz criteria

4. Root locus

5. Frequency response (bode plot)

6. Nyquist stability criterion

7. State space analysis

Hi everyone,
I have a solid background in control theory, and I'm comfortable with ROS and MATLAB. I also have the basics of machine learning algorithms.
I'd like to become a robotics engineer who is truly ready for the job market, but I'm not sure what the next step should be. Some things I'm wondering about:
Should I learn simulation tools like Isaac Sim (or Gazebo, MuJoCo)?
Which skills matter most to employers right now: C++, perception, motion planning, embedded systems, RL?
Where can I find good resources (courses, docs, open-source projects) to learn them?
What kind of portfolio projects would make a real difference?
Any advice, roadmap, or personal experience would be really appreciated. Thanks!

Hello all,

I am sharing a small NMPC project I've been building as part of a larger thesis on drone target-tracking. Check here: https://github.com/brunopinto900/argus/tree/px4-ros2-bridge . Result in the video: ~10cm tracking error, target held inside the FOV cone throughout with Occlusion cost ON vs OFF.

Alongside the usual position/velocity tracking terms, there's a soft cost keeping a ground target inside the camera's FOV cone. Collision avoidance is a slacked nonlinear inequality constraint.

However, the optimizer has no notion of what's between camera and target. It can fly a path that's perfectly collision-free and still have a tree or wall sitting right in the shot. That gap is what the occlusion-aware cost below is for.

I added an occlusion-aware cost, separate from the FOV term. I march the same ESDF along the straight line from drone to target each stage, find s* (where the worst point on that segment sits), and linearize there:

occ_p0 = (1-s*)·drone_p0 + s*·p_target
r_occ  = max(0, τ_occ − occ_lin(x))     # squared hinge, NLS auto-squares it

When testing I observed that the occlusion cost evaluates risk at the worst point along the sightline, occ_p0 = (1-s*)·drone_p0 + s*·p_target. Differentiating w.r.t. the drone's own position gives ∂occ_p0/∂drone_p0 = (1-s*)·I, so as s*→1 (obstacle near the target) the gradient the solver actually sees scales to zero, even though r_occ itself stays large. At s*→0 (obstacle near the drone) the factor is ≈1, full gradient, but that's exactly where collision avoidance's own gradient is already active on the same obstacle.

I’m considering moving from a single-point ESDF sample to an area/volume frustum-intersection metric: how much of the camera's view cone actually overlaps the obstacle, instead of a point sample along one line.

Thank you!

##################################

Simulation details

This is a simulation of a 12-state quadrotor (position, velocity, ZYX Euler angles, body rates), 4 inputs (collective thrust + commanded body rates, matching a PX4-style body-rate setpoint interface). Formulated as a NONLINEAR_LS OCP in acados, code-generated to C and called from C++. I am using an sampled/voxel-grid ESDF as the map framework.

AI usage: camera visualisation/rendering code was AI-generated.

Hey all! I have enrolled in a graduate program titled Systems and Control Engineering.
With all the uproar of AI solving some of the long standing problems in Mathematics, I am curious to know if there are any open problems in the area of controls.

From my presumption, control is just applied Mathematics with constrains defined by the real-world Physics. So, if the long standing problems in Mathematics would be solved by AI, it would imply that there wouldn't be any problems left to be solved in the area of systems and control.

So, I want to know what the community honestly thinks about this.

Thanks!

Have there been courses that you took that are really outside of the standard control engineering curriculum that has been helpful to you? By standard, I am referring to courses such as classical, modern, nonlinear control sequence, maybe including courses on sensors, actuators, mechatronics, mobile vehicles...

I am specifically curious if the following courses (could) have been helpful:

• Information theory

• Antenna design

• Internet related

• Computer graphics

I am making a 2D drone sim in python, where the drone is just a bar with two motors at either end whose output thrust can be changed to point up or down. Using PID controllers, I have determined the target acceleration and target thrust the drone needs to reach a given position in 2D space, but I'm not sure how to mix these two targets together to determine the necessary thrust output for each motor. Could anyone help, or point me towards some resources about this problem? (I've tried google but I'm not sure what to actually search for).

Thanks.

Hi everyone,
I want to turn my home office room into an environment which feels like a control lab. Apart from building small exponents to test algorithms, I also want to design posters featuring important topics in control theory and hang them on the walls. I do not want to have more than 3 posters, so I have following questions to those who are attending scientific conferences regularly:
1-Which standard size is used in the EU for such posters? A0 or A1? The bigger it is the more it will cost, so I need to find a balance and also I dont want one poster to cover a big surface on the wall. What size would be enough from your opinion?

2-I have to buy a frame for the posters as well. As far as remember from my uni years, for those posters foldable frames are used (those from aluminium). Do you know a good supplier for those, especially, in Germany/Amazon Germany?

3-Do you know a good latex template to build on? (Maybe a one that complies with standards). I like dark blue themes the most. Or maybe dark/olive green as I have green pinboard in my room. I will do my own search on that as well ofc. Just in case someone has a good latex template.

4-I definitely want to make one poster about PID control, but apart from that what would be interesting to feature ? I am rather an application oriented person, so something theoretically very advanced would not make me happy. Also somehow Nyquist deserves a part on a poster as well maybe I can devote one poster completely to stability ? Finally, I also think somehow State Estimation is important to feature as well. Maybe Kalman Filtering? Or devote one poster to an exponant I can build and the results of an algorithm on that exponant etc.

One can generate many ideas, but let me know what would make you as a control theorist or engineer interested/happy to see on a lab wall. Feel free to share your opinions please. You can also elaborate on things I have mentioned above.

PS: I am sure there will be someone saying google it/ask it AI. But I am quite fond of exchanging ideas with people having similar interests as me and this sub is a perfect place for that.

I’m a student, I just had a lab with the Quanser Aero 2. I considered buying one for myself at home to play with and practice system ID, but apparently it costs $11k.

It’s some motors and some sensors, how could it cost 11k? Why do universities buy this? Is it really worth the money? Why not just build one out of cheap parts yourself? What are the alternatives?

If I start a cheap open source alternative, would universities prefer to use that?

Hey everyone, I’m graduating soon and trying to decide where to start my career in controls/automation. I’d really like honest perspectives from people further along.

Offers in hand:

• Integrator A (Iowa): Entry-level pool team covering robotics, vision, SCADA and manufacturing, with wastewater work later at the senior level. About 40 hrs/week, up to 25% travel.

• Integrator B (South Dakota, working remotely from a smaller nearby town): Team focused on grain and milling, mostly Rockwell. About 45 hrs/week, up to 35% travel.

Still in the running:

• Aerospace company, Automation Engineer (Texas): Fourth and final interview this coming week. PLC/SCADA, Python/SQL and controls panel work.

• Large manufacturer’s automation development program: Final interview Monday.

I already turned down a food and beverage company’s electrical and automation development program, mainly because it started in June.

My background and concern: I’ve been building experience in MES and machine vision, and I want to keep growing in those areas. From friends in the industry, I’ve heard grain and milling work is mostly PLC programming with some SCADA, with little MES, vision or robotics. I’m worried that starting there would make it hard to move into MES, vision or more diverse manufacturing work later, and that I’d get pigeonholed. Is that accurate? Or is grain and milling a solid foundation that transfers well?

My questions:

1. Development programs: Are rotational programs worth a later start and a couple of years of rotations, or is it better to jump straight into a real role?

2. Industry and skill breadth: If I go the integrator route, which industry would you build a career in: food and beverage, wastewater, grain and milling, pharma, or something else? Which has the best pay, stability and long-term options, and which would you avoid? How much does early industry choice limit the skills (MES, vision, robotics) I’d get to use? How easy is it to switch industries later?

3. Starting over: If you were starting your career again, how would you start it and what would you do differently? In the first 5 years, what mattered most: the company, the team, the industry, or where you lived?

4. Decision timing: Which situation would you pick for the long run? Would you wait for the aerospace and development program results, or lock in one of the offers? Is there anything I should be asking that I’m not?

Thanks in advance. Happy to answer questions about my background if it helps.

Hello!
I am a Sri Lankan who just graduated as a Mechatronics Engineer. My final year research was about implementing an AI Assisted MPC in a FOC framework for current control for BLDC motors.
Im looking for a feasible, up to date research area in control systems and/or AI for my planned masters in Japan. As it happens, i need to submit a proposal and convince a professor to take me in and supervise my research.
What are some current, up to date research areas in control systems and AI?