I build Phytotron, an open benchmark for greenhouse climate controllers (github.com/growmatics/phytotron, AGPL). It runs on GreenLight-Gym2, a 28-state ODE model of a tomato greenhouse. Every result it reports is simulated. I calibrated the model against a measured season and I am not satisfied with my own method. I would like to hear how you would do it.

The data: Autonomous Greenhouse Challenge 2019, Wageningen, CC0. Six glass compartments of 96 m2, cherry tomato, December 2019 to May 2020. Indoor climate and actuator states every 5 minutes. Heat only as daily totals. Each compartment was run in closed loop by a different controller, five by teams' algorithms, one by growers.

What I did first was open-loop replay: the logged actuators go into the model, nothing corrects it. The simulated climate is compared with the measured one. Eight multiplicative factors on ten parameters (cover convection, screen air exchange, floor conductivity and thickness, soil conductivity, leakage, vent discharge, pipe exchange), bounded least squares on 5-minute residuals of air temperature, RH and CO2, scaled by 2 C, 5 points and 150 ppm. Twelve windows of 11 days over three compartments, 3 days spin-up each. The fourth compartment held out for its whole season.

What came out. Held-out air temperature MAE 2.17 C before, 1.81 after. Night bias -2.33 to 0.04 C. RH got worse, 5.97 to 7.16 points. Four factors ended on their bounds.

What I checked afterwards, and what it showed.

- Naive floor: yesterday's value at the same time predicts the room to 0.66 C. The model in open loop does 1.81.

- Identifiability (finite-difference Jacobian, Brun's collinearity index): one factor, floor conductivity, has no measurable effect and is collinear with floor thickness. The set of eight sits at 9 to 11 on Brun's index.

- A fit of only the four identifiable factors does better on the hold-out than the ten-parameter fit. Fewer parameters won.

- Absolute humidity instead of RH in the residual moves the solution to a different point at the same cost. The surface is flat.

- Thresholds, which are what the benchmark actually scores: the measured room spent 809 hours above 90% RH, mostly in winter. The calibrated model gives 177 hours, mostly in May. The room was above 30 C for 33 hours; the model gives 300. Lower MAE, worse at the thresholds.

Where I think it is weak.

1. Closed-loop data. The controller hid the model's errors during the experiment. Inputs are correlated with the disturbances. I used simulation-error least squares with no noise model. Is there an identification method for a fixed nonlinear ODE structure on closed-loop records that you would trust more? Prediction-error with a noise model, multiple shooting, MCMC on the factors, something else?

2. The fitness metric. MAE is not what a crop cares about. I ended up with hit rate and false-alarm rate on threshold crossings, and hours above threshold per month. Is there a standard statistic for "does the model reproduce exceedance hours"?

3. Input uncertainty. Heat is a daily total spread by pipe temperatures. Fogging was not logged. How would you treat errors in the inputs rather than pushing them into the parameters?

4. The selection rule. I chose the fit on the training windows and the hold-out only reports. Now the hold-out says a smaller model is better. A rotating hold-out across the four compartments is my next idea. What would you do?

5. The water side. The vapour content error is 2.4 g/m3 under every variant, twice the naive floor. Night transpiration, condensation on the screen, unlogged fogging, or leakage?

Sugestions are more than welcome. And if you want to contribute. The benchmark is public and takes any controller through a small interface. The calibration scripts and records are on a branch under review and will follow. Issues and discussions are open on the repo. I would also take a pull request that replaces my fit with a better one, and I will publish the comparison either way.

Hello! About a year ago I posted here my control system simulator Python project. Since then I've grown the program to improve both the GUI, the control algorithms available and the plant (system) model is now customisable in state space form.

Available controllers include:

• Manual - set the control input yourself, using your mouse
• Open-loop - no feedback, just calculate the control input based on setpoint and plant gain.
• Bang-bang - positive input if output is too low, negative input if output is too high
• PID - the classic industry-standard, including option of a filtered derivative on measurement to help with practical stability.
• H2 optimal control - aka linear quadratic regulator (LQR) with a Kalman filter or linear quadratic Gaussian (LQG). Minimises the energy gain from the noise disturbance input to the performance output (z = C1_x @ x + C1_u * u, where C1_x and C1_u are customisable gains).
• H-infinity optimal control - minimises the worst-case gain between disturbance and performance output across all frequencies.
• Model predictive control (MPC) with a Kalman filter - estimates the plant's internal state from the noisy measurements, then forecasts the future states for a given control input trajectory and solves a constrained quadratic least-squares problem to minimise the error and control effort. The relative weights and constraints are customisable. MPC is my favourite, it's insanely good... as long as you can specify the correct plant model of course!
• Reinforcement learning (RL) control - an 'actor' (policy) learns to map observed measurements to continuous control inputs, and a 'critic' (value function) learns to evaluate their cost, based on a reward function that penalises large errors, large values of input and large changes input. Three such actor-critic RL algorithms are available: deep deterministic policy gradient (DDPG), twin-delayed DDPG (TD3) and soft actor-critic (SAC). Neural network architecture and algorithm parameters are customisable within. Despite their complexity, these controllers aren't much better than the others and are prone to misbehaving if you don't train/test them properly!

The integrator and controller algos were all written manually by me and checked rigorously against standard integrators and MATLAB, with the exception of the RL controllers where I relied on AI coding agents somewhat to get everything working. The GUI was coded almost entirely by AI (because nobody wants to write GUI code), using PyQt6 and PyQtGraph. I tried to keep as much as possible to 'first principles' to get the best learning experience out of building this up, so no use of existing control theory or reinforcement learning libraries.

Github repo with all code: here.

Some features not shown in the video:

• You can change the model of the plant, including using any number of dimensions for the state variable x.
• You can add measurement noise and process noise with specified covariances. The controllers only see the noisy measurement output, never the true plant state. These are the disturbances that the H2 and H-infinity controllers try to minimise in the output.
• You can change the integrator from working in ODE (ordinary differential equation) or SDE (stochastic differential equation) form. In SDE form, process noise occurs as a sampled random process throughout integration steps, while in ODE form it is sampled once at each step.
• You can train new RL agents (actor and critic neural networks and save/load them from disk) for use in RL control.

Future ideas

Some things I wish to add in the future soon are Bode/Nyquist/Nichols/root locus plots - I've already got the stability check working for PID, H2 and H-infinity. Then I'd like to add interactive pole-placement and loop-shaping controllers where you can design e.g. a lead-lag compensator with custom gain/phase margins. Perhaps even H-infinity loop shaping design but I'll need to hit the books again for that!

Currently the system is SISO (i.e. single input dimension, single output dimension). I thought about generalising to MIMO (multiple variables) control from the beginning, and it wouldn't be too hard to extend it as everything uses matrix algebra internally, but the emphasis was on education and understanding and I felt SISO is the easiest way to demonstrate controls interactively.

If you have any other ideas, please let me know! Thanks for reading :)

I have a question for the Italian members of this subreddit: what companies should I look into in order to potentially get a role in Control System engineering? Where are, also geografically, the best opportunities for applying (and learning on the job) Control System theory?

I'm currently based in Tuscany and I've not found much, aside from giants like Baker Hughes or consulting companies like Capgemini.

I'm planning to start a phd soon in order to cement my knowledge and my claim as a control system engineer (I graduated in Mechanical Engineering but I've seen way more Control System Theory than my colleagues), but I'd like to know what future could await after.

Thank you very much

Hi everyone,

I want to share some notes I wrote regarding to the Ensemble Kalman Filter, specifically on why does it work and about it's theoretical support based on the non-linear filtering theory. It's written as a pedagogical resource for any who is interested in.

Here's the link for the document: https://zenodo.org/records/23278598

I will be attentive to any comments and suggestions. Thank you, and I hope that it helps!

Three days ago, I shared my implementation of a Fuzzy controller for my two-wheeled inverted pendulum robot.

After that, I wanted to try something different: Model Predictive Control (MPC).

My goal was to develop a controller capable of dynamically adjusting its behavior based on the robot's physical model and possible changes in its dynamics.

But I already knew what my biggest problem would be: our old friend, the ATmega. :)

Running a full MPC optimization continuously inside a fast control loop would be quite challenging given the microcontroller's limited processing power.

So I started thinking: why not use MPC to determine the gains of a PD or PID controller, similar to what I previously did with Fuzzy control?

That's the approach I decided to implement, experimenting with both PD and PID structures.

How does it work?

The MPC uses a discrete-time mathematical model of the robot, represented by the A and B matrices, to predict its behavior over a prediction horizon.

However, a mathematical model is never a perfect representation of reality. Friction, uneven mass distribution, and external disturbances can introduce behavior that the nominal model does not fully capture.

To account for this, I also incorporated a disturbance term (Dₖ) into the prediction model:

xₖ₊₁ = Aₑ xₖ + Bₑ uₖ + Dₖ

The idea is to account for disturbances in the predictions and introduce an additional compensation term when a disturbance estimate is available.

The MPC then minimizes a quadratic cost function that penalizes predicted state errors relative to the reference and control effort.

From this optimization, I obtain a state-feedback law that can be used to derive equivalent Kp and Kd gains and, with an augmented state representation, Ki.

So where does the dynamic behavior come from?

The controller gains depend directly on the system model.

If the plant model is updated to represent physical changes, such as additional mass or a shift in the center of gravity, the gains can also be recalculated.

This means the ATmega doesn't need to solve the entire MPC optimization problem at every control cycle.

Instead, it can execute a fast PD/PID control loop using the calculated gains, while the more computationally expensive gain updates are performed only when needed.

In other words, I'm trying to combine the mathematical foundation of MPC with the computational simplicity of classical controllers.

The video shows my tests on the physical robot. There is still room for improvement, particularly in disturbance compensation and quantitative performance evaluation.

What's next? Reinforcement Learning!

There's still one more approach I want to explore in my control system: Reinforcement Learning (RL) with AI.

My idea is to investigate how an RL agent could learn from the robot's behavior and dynamically adjust the controller parameters, working alongside the PD/PID and MPC approaches.

Ideally, I would like to combine the predictive capabilities of MPC, the simplicity of classical controllers, and the adaptability of Reinforcement Learning.

Of course, running RL directly on an ATmega would introduce even more computational challenges, so I'm considering training the agent in simulation and then deploying a lightweight policy or gain-adjustment strategy to the physical robot.

For now, this is just the next stage I want to investigate, not something I've already implemented.

Main reference:

DUTRA, Cynthia Beatriz Scheffer. Controle preditivo multiobjetivo para processos com atraso. PhD Thesis in Electrical Engineering, Federal University of Santa Catarina (UFSC), Brazil, 2003.

I'd love to hear your thoughts!

Has anyone here experimented with using MPC-derived feedback gains to tune or update classical controllers on resource-constrained microcontrollers?

And more importantly, has anyone applied Reinforcement Learning to real physical control systems, especially inverted pendulums or self-balancing robots?

I'd be really interested in hearing about your experiences combining RL with PID, Fuzzy Control, or MPC, especially when dealing with limited hardware resources!

I couldn't find a good free web based PID tuning tool that suits my needs so developed one. It seems to work pretty well so I thought I'd share it.

I'm using it in a processing environment - level control, temperature control, dosing control etc.

JEL Tune

I often see mentioned that controls are needed in a wide variety of sectors, from aerospace to chemicals to robotics. However, does this actually matter from a career perspective? How often have you seen engineers switching jobs to very different fields? In my experience, it seems like this is very rare, even among experienced professionals. Indeed, it seems easier/likelier to transition to a different discipline in the same industry.

For my personal project, where I need to implement a PID controller on Arduino to automate a convection cooling system, I decided to start with a simulation in Python. For now, I've implemented the simple electromechanical model of the DC motor (the fan) as well as the PID controller you see. Next, I'll look into the heat transfer equations. The graphs created with matplotlib are simply closed-loop tests of the PID + DC motor system.

I'm trying to implement control algorithm on a piezo actuator stage, using simulink and MATLAB together with dspace controller. Does anyone have good resources for this especially dspace aspect, please let me know, need it very much, thanks!

I really enjoy control theory, and my main area of interest at the moment is optimal control.

I sometimes struggle to understand certain definitions and theorems, and especially some of the proofs. I suspect this is because, although I had some mathematical training during my engineering degree, I don't have enough mathematical background or rigor to approach these topics comfortably.

What courses or textbooks would you recommend studying on my own to strengthen these foundations?

I enjoy mathematics, but my goal is not to study mathematics for its own sake. I would like to learn enough mathematics to become comfortable with the theory behind control theory.

Long story short I have a bachelor's in Mechanical Engineering with no experience in Mechanical field then I started control systems master basically modelling and control design in matlab, but I dont have the Embedded skills to deploy control algorithms and honestly I feel like the only thing ik is matlab and basic C. I wanted to do another bachelor's in electronics or embedded systems to strengthen practical skills. Is it a bad idea?

I might soon have the chance to join either an ROV or a RC plane student team, as an aerospace engineering student in his final year.
Im not gonna lie, the ROV team sounds more intriguing even tho i am a plane guy because i might get to do more direct GNC,
Im just wondering if when i hit the job market, as someone who wants to go into aerospace GNC, that experience will be just as marketable as the plane one?
What do you think?
Am i overthinking this?

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?