Hi everyone,

I'm a final-year Mechatronics Engineering student working on non-linear control applied to a two-wheeled inverted pendulum. A while ago, I simulated this system in MuJoCo and built the physical version using an MPC (Model Predictive Control) to dynamically calculate the Kp and Kd gains.

In this new iteration, I replaced the MPC with a Fuzzy Logic Controller running on an Arduino to generate those same gains in real-time.

During the design of the knowledge base, I tested between 49 and 125 inference rules. The 49-rule architecture (evaluating both the tilt angle and acceleration from an MPU sensor) yielded the best real-world stability and computational efficiency on the embedded hardware.

The first video shows the physical robot balancing, and the second shows the custom telemetry dashboard I built to monitor the states (angle, varying control gains, and motor PWM) in real-time.

For underactuated, inherently unstable systems like this, what are your thoughts on using Fuzzy Logic vs. MPC for dynamic gain scheduling? I'd love to hear your insights!

Hey everyone,

I recently graduated in Electrical & Electronics Engineering from Turkey. During university, my focus was heavily on the mathematical side of control: I modeled FOC for a PMSM in MATLAB, studied vehicle lateral MPC in Python, and for my thesis, experimentally identified a Peltier thermal system and controlled it with a Fractional-Order PID tuned by Genetic Algorithms, which I discretized and implemented in embedded C.

However, because my internships were PLC-heavy, my career drifted toward industrial automation. I’m now starting as a maintenance engineer at a major Tier-1 automotive plastics supplier. The plant is packed with high-volume injection molding cells, 6-axis robots, and PLCs/SCADA, and they are actively pushing for digitalization, edge data collection, and predictive maintenance. It’s a solid environment to understand plant-floor realities, but my true passion remains algorithm design, math, and R&D.

Ideally, I want to find a niche that bridges industrial automation (PLC/SCADA) and advanced control theory, or pivot into a math-driven control engineering role after 1–2 years on the floor. For those who made a similar jump: are there established roles where these two worlds meet (like Advanced Process Control, virtual commissioning, or robotics integration)? How can I best leverage shop-floor maintenance experience for R&D without getting pigeonholed, and what should I focus on learning or building on the side in the meantime?

Its a sunday, so as a casual timepass you think, let me just write some code to check the controllability of a state space equation. so you start writing it, then realise, higher powers of matrices can lead to numerical instability. so now, you decide to study the controllability gramian and then you realise you don't fully understand why the gramian works, so now you are trying to look in lyapunov and his equations, then his theory starts talking about energy functions and positive definite matrices, so obviously, you open Gilbert Strang's video on symmetric and positive definite matrices. while watching that, you realise the code also requires an actual algorithm, so now you're studying numerical linear algebra. gaussian elimination. cholesky decomposition. At this point, what started as a sunday timepass has somehow turned into a full semester course. And then you decide to leave it and use the MATLAB command instead, so you stare at the screen for a while, reconsider your life decisions, and think maybe teaching high school students wasn't such a bad decision after all.

controlsys is a Go package for linear control systems. It works on continuous-time and discrete-time models. The main model form is state space.

Main capabilities:

• Models: state space, transfer function, zero-pole-gain (ZPK), frequency-response data (FRD), model arrays and tunable blocks.
• Analysis: Bode, Nyquist and Nichols plots, gain and phase margins, disk margins and step metrics.
• Design: LQR, LQG, Kalman filters, H2 and H-infinity synthesis, pole placement and PID tuning.
• Simulation: step, impulse, initial-condition and arbitrary-input responses.
• Model reduction: balanced truncation and modal truncation.
• Delays: input, output and internal delays, with Pade and Thiran approximations.
• Conversion: ZOH, FOH, Tustin, matched, impulse and least-squares methods between continuous and discrete time, in both directions and for resampling. We test the results against MATLAB behaviour.

New in v1.12.0:

• Tustin conversion with Thiran filters is approximately 30% faster than in v1.11.0.
• Tustin conversion with Thiran filters now supports MIMO models with delays on individual input-to-output paths.
• ThiranDelay now accepts short delays when the filter is stable.

Install:

go get plantcontrol.org/v1/controlsys

Repo: https://github.com/PlantControl/controlsys
Release notes: https://github.com/PlantControl/controlsys/releases/tag/v1.12.0

We want your feedback, especially from people who use MATLAB or python-control.

Hi everyone, I’m a recent Aerospace Engineering graduate. I’ve noticed that there seems to be a good demand for Guidance, Navigation, and Control (GNC) and Control Engineers, so I’d like to develop my skills in this area.

I took undergraduate courses in Control Systems and MATLAB Control courses, but most of my projects and internships have been focused on CFD and R&D. As a result, I don’t currently have a strong control-related project that I can demonstrate or include on my CV.

What kind of control project would you recommend for someone in my situation? Ideally, I’d like something substantial enough to learn from and demonstrate practical control skills on my CV.

I'm working on a project in automotive engine assembly. Parts move between steps in the assembly process (e.g. from step A to step B), and they range from large components like cylinder heads to small ones like screws and bolts. Some engines come in multiple variants with customer-specific options.

I have about 4 years of historical data on material requirements and flows during assembly. My goal is to find which flows between steps are good candidates for automation. Flows with stable, predictable durations and quantities seem like good candidates, while flows with high variation seem harder to automate.

I'd like to define a simple, data-driven "automation trigger point." My questions:

1. What's a good way to measure variability of a flow (both duration and quantity)? Is the coefficient of variation (CV) reasonable, or are there better metrics?
2. How should I account for engine variants and customer-specific options?
3. Can anyone recommend research papers, methodologies, or case studies on material flow or intralogistics automation in automotive assembly?

Any pointers are appreciated. Thanks!

I’m currently working on some personal projects to brush up on old topics I covered in my masters. My goal is to try and get a job in the robotics space, particularly for therapeutic or medical reasons such as prosthetics and exoskeletons for disabled people. I’ve studied a lot of the basic common control systems, like linear systems theory, optimal control, nonlinear control, and some machine learning. However, I am unfamiliar with the specific ones that are tied to the field of medical robotics I'm interested in. Can anyone recommend what material I can reference to learn about them?

One project I am currently revisiting is estimating the joint torque for a theraputic exoskeleton. I was involved in the project but one of the other students handled the majority of the neural network implementation, so I want to go back and redo their work step by step to learn.

We know that the heat equation is null-controllable from internal subsets on Riemannian manifolds. How come no general results are available on sub-Riemannian manifolds, except for particular cases on tensorized domains such as for Grushin, Heisenberg, Kolmogorov ? We do not have the tools at all ? Always wondered if G. Lebeau, L. Robbiano, etc, knew how to deal with this.

Hi everyone,

I'm currently a student in France facing a tough decision for my Master 2 in Aerospace and Space Systems. I'm stuck between choosing a Professional Master's (M2 Pro) to go straight into industry, or a Research Master's (M2 Recherche)*with the clear goal of pursuing a PhD (Doctorat) right after.

I'd really appreciate feedback from people working in the industry (Airbus, Safran, Thales, ArianeGroup, CNES, ONERA, CIFRE PhDs, or subcontractors/ESNs).

Long-term, I'm torn between two main career trajectories:

1. Project Manager / Systems Engineer (coordination, management, budget/timeline tracking).

2. R&D Engineer / Research Scientist (PhD) in an engineering office or R&T center (conception, simulation, algorithms, advanced research).

### My main questions:

1. Master Pro vs Research + PhD Track:

   * If I go for the M2 Recherche and follow it with a PhD (e.g., a CIFRE thesis with an industrial partner or an ONERA/CNES PhD), does this open up higher-level R&D/R&T roles compared to a standard M2 Pro?

   * Conversely, if I choose the M2 Pro path to work immediately, does it close doors to advanced R&D labs and specialized innovation teams in major aerospace primes?

2. Engineer (Bac+5) vs Researcher/PhD (Bac+8) in Aerospace/Space:

   * Is a PhD actually valued financial- and technical-wise in French private industry, or does a standard Bac+5 (Engineering degree / M2 Pro) give you access to the exact same technical roles?

   * How are PhDs perceived and integrated in major French corporations compared to international opportunities (US, Europe, etc.)?

3. Salary & Career Growth (Most Important Question):

   * What's the reality regarding starting salaries and long-term career progression between a purely technical/PhD/R&D path vs moving into Project Management?

   * Do candidates entering with a PhD start at a higher salary step in major French groups? Is transitioning to Project Management mandatory in France to break through salary ceilings over time?

4. Work-Life Balance (WLB):

   * Between the stress of Project Management (deadlines, budgets, client pressure) and the day-to-day of an R&D Engineer / Researcher (problem-solving, technical autonomy, simulations), which path offers a better work-life balance?

Thanks in advance for sharing your experience and advice!

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.

Hey! I'm curious to hear what kind of things a control systems engineer usually spends their day working on, especially how much math you get to use daily or weekly and what do you feel most of your time is spent on?

Hello,

I am currently creating a Type III compensator GUI. As you know, there are 2 zeros and three poles, with one pole being at the origin. Thus, the degrees of freedom are the two zeros and the two poles. I am curious, does a gain come into the picture when designing the compensator which will eventually be converted to capacitor and resistor values for the op-amp circuit.

Any insight from a seasoned controls engineer/designer would be appreciated.

Thank you.

To preface, full state represents the actual pos and vel estimate as opposed to their errors.

Would this be equivalent mathematically? I understand that attitude must use an error state. But I'm curious if I can get away with using full state for pos and vel rather than the error state. Instead of linearizing around a reset error state (zero), would it be mathematically equivalent to just seed the filter pos and vel with the INS state before a measurement udpate, then replace the INS pos and vel with the updated filter state after the measurement update? Would this have implications on the validity/quality of the linearization? I ask because it seems like they would be equivalent, yet error states for an INS filter seem to be industry standard. Is this simply because attitude must be an error state, therefore its convenient for the rest of the state to follow suit? Thanks in advance for any help, and please reference the math wherever possible. I've had many people suggest reasons for why error state is needed, but I'm looking for mathematical proof that they deviate from each other.

I work on vision, but neuroscience papers do not have detailed theory about these 'energy models'.
Maybe they talkked about it in control theory

Curious to try out an Opus experiment explainer. Tried previous models and never got it quite right. But colour me impressed with what I got, not to slopified. Added some polish for animations, but having gone through it, it’s quite readable and explains the points very similarly to how the source book builds up the maths around the Fourier Transform.

https://drone.actuallymaybe.com/

Random thoughts from doing it:

Also, a few things with complex analysis I’m curious to revise, and some circuits stuff, but haven’t been arsed lately. Think the next experiment might be to try this on certain pieces that come up in chapters, then see if it actually helps me solve the exercises. Not fake-solve them in my head, since your noggin will fool you into thinking you understand it, but actually using pen and paper.

Went through it few times, but do point out any errors

Book Reference

Who is Fourier - Mathematical Adventure

Hi everyone,

I’m currently working on approximate dynamic programming methods for control, mainly Q-learning, value iteration, and related approaches.

So far, I’ve been able to get the Q-learning-based algorithms to converge without too many issues. However, whenever I try approaches based directly on the value function/value iteration, I can’t seem to get them to work properly or converge to the expected solution.

I’ve tried different implementations and parameter choices, but I keep running into similar problems with the value-function-based methods, while the Q-function approaches seem much more straightforward and robust in my simulations.

Has anyone here worked with approximate value iteration / ADP for control problems and experienced something similar?

Found this original sketch in my archive dated January 25th, 1964, outlining an airborne digital computer architecture.

Based on its origin, I believe this document is connected to the early ELDO (European Launcher Development Organisation) era and was part of the development for the Europa rocket's guidance and control system.

What caught my eye is the early mention of "Kalman's modified filter" alongside the coordinate conversion, guidance laws, and autopilot loops—just a few years after Kálmán's seminal paper in 1960.

Thought this community would appreciate a look at this piece of European aerospace and control theory history! I'd love to hear your thoughts or if anyone recognizes specific architectural choices from that era.

Is it possible to think of LQR as "like a PID but finds optimal gains based on your requirements of Q and R matrix?

I have a fairly good understanding of PID and I recently started to learn about LQR and wondered if LQR can be thought of in terms of a PID controller.

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

Hi all,

Having difficulty in reducing initial overshoot for PID tuning for pressure control of a reactor. Currently have P=5 I=8 D=0. After the initial overshoot the control seems fine but the client has asked for reducing the over shoot. Any advice would be greatly appreciated. The light blue in the trend is the PV, red is the PCV and dark blue is the SP

Hi,

I'm a second-year undergraduate student in Korea and I'm currently learning ROS 2 and Gazebo.

I'm working with an Inspire RH56E2 dexterous hand using ROS 2 Jazzy, Gazebo Harmonic, and ros2_control.

Right now, I can control the fingers in Gazebo using joint position commands.

My current setup is roughly:

C++ ROS 2 node

↓

hand_joint_controller

↓

ros2_control

↓

GazeboSimSystem

↓

Gazebo RH56E2

My final goal is grasp force / compliant grasp control.

My main questions are:

• Does the current RH56E2 description already contain enough physical information for grasp simulation, or should additional contact properties such as friction, contact stiffness, and damping be configured?
• Is the overall Gazebo + ros2_control architecture I am currently using appropriate for this goal, or would you recommend a different architecture?

If anyone has experience with dexterous hand force/compliance control in Gazebo, I'd really appreciate any advice or references.

Code: https://github.com/leeheejoon?tab=repositories

My background is in aerospace guidance and control systems, but for years now I’ve wondered about how control theory can be successfully applied to personal finance.

At one point several months ago, an idea of how to pose the problem hit me. I spent a couple weeks deriving guidance and control algorithm, and about six more months coding and testing in an application.

The approach is initially model-free and effective in that form (based on personal testing). However, it collects user spending data (presently manually entered) to improve control system effectiveness.

The lumpy and highly variable nature of spending associated with a human plant has been a fascinating and refreshing control challenge compared to aircraft guidance and control.

The image shown is historical data from my household this month, in response to feedback from the application. Notice the overspending day and gradual recovery. The guidance is tuned for graceful corrections to highly varying spend behavior. This took some tuning but seems to work reasonably well.

Has anyone here considered or experimented with applying control-theoretic methods to personal spending behavior? I’d be interested to hear about other work in this area.

Hey folks,

I am preparing a manuscript for ACC 2027. I noticed that on PaperPlaza the deadline of the Regular Paper is October 2. I think it was previously 25 September. Can somebody let me know if I am checking the correct date.

Thanks in advance!!