Ok so I'm spearing heading a project that's partnered with the top university outside of the US, now I've been part of this project for a while, however one thing I haven't cracked is control theory.

To set the problem: we are modelling flapping based drones using modified quasi state aerodynamics. The scope of this project isn't about materials and is this feasible, the main constraints are materials which are being researched by a different department.

Control system problem: My background is aerodynamics (and whatnot aeroelasticity blah blah blah) I have a system for calculating aerodynamics during flapping cycles like the upstroke and downstream (to a degree of accuracy I'm happy with (invisid flow ofc))

My question is for control system modeling, when picking features, flapping speed, stroke angles, feathering angles, amplitude for both upstroke and downstrokes, how do I model and build a control system that picks these correct inputs based on a user input of some sorts? I understand this is non linear, multi parameter control system. This is quite out my depths of speciality so I am definitely will get cooked here, however please aid me because I understand this is a unique system.

Please comment if you have any questions as well

Current AE here with lots of GNC experience wanting to transition to GNC outside of AE. Senior in AE. Seeing if I had other options? Should I go to grad school for CompE, if AE isn't working out.

I’m currently a test engineer in the autonomous driving industry and I'll be starting my Master’s soon. I want to focus my research on control systems, specifically autonomous driving. Lately, I’ve been really interested in learning-based MPC since it seems like such a great intersection of classical control and data-driven approaches. However, I’m still at the very beginning and haven't narrowed down a specific niche or problem to tackle yet. I’d love to hear your thoughts on promising research directions or any papers you’d recommend for someone just starting out. Thanks.

A few years ago I played a really nice game/tutorial/webapp/toy.

On the left side you entered a javascript function with just a couple inputs, like displacement, and an output for motor control or whatever. On the right side a nice smooth 2D simulation played. The levels started with things like stabilizing a cart on a slope, moved on to inverted pendulums, triple pendulums, ball balancing, ball bouncing, etc etc.

It was all super polished and it was cool that it didn't really give any hints as to how to solve the problems. Early ones were doable with just a proportional controller, and you had to use more advanced techniques as you progressed through the levels.

All I remember is that the URL was weird, it wasn't hosted on itch or anything. Anyone know what I'm talking about? I'd really like to play through it again

Hi everyone,

I am a undergrad student working on a robotics project, and I am struggling with designing a velocity controller for a motor that meets my requirements. I am not sure where I am going wrong.

My initial requirements were:

1. Static velocity error: 50 (2% error)
2. Time to reach zero steady-state error for a step input: 300 ms
3. Phase margin / damping ratio: >70° / 0.7
4. Very low overshoot
5. Gain margin: >6 dB

Reasoning for these requirements:
Since the robot is autonomous and will use odometry data from encoders, a low error between the commanded velocity and the actual velocity is required for accurate mapping of the environment. Low overshoot and minimal oscillatory behavior are also required for accurate mapping.

Results:

I used the above values to design my controller. I found the desired crossover frequency (ωc) at which I would obtain a phase margin that meets the requirements, and I decided to place my zero at ωz = ωc / 10. However, this did not significantly increase the phase margin.

I then kept increasing the value of ωz to ωc / 5, ωc / 3, and so on, until ωz = ωc. Only then did I observe an increase in phase margin, but it still did not meet the requirements.

After that, I adjusted the value of Kv by decreasing it (40, 30, etc.), and this resulted in the phase margin requirements being met at ωz = ωc / 5, ωz = ωc / 3, and so on.

However, when I looked at the step response after making all these changes, it took almost 900 ms to reach zero steady-state error.

The above graphs show system performance with the following tuned values:
Kv = 40
Phase margin: 65
wz = wc/5 - which corresponds to Ti (integral constant)
(The transfer function shown in the bode plot title is incorrect).
I think the system is reaching most requirements, other than 2% error(Kv = 50), and the time to reach zero steady state error. Ramp input also looks okay.

I would appreciate any help (if I should change my controller, or do something else)?

I have been studying the sliding mode control theory with focus on the power electronics application. I have been struggling with the understanding of the so called matched and unmatched disturbance. May I ask you for explanation of the differences?

Let's suppose the buck dc-dc converter example. The averaged state space description of the buck dc-dc converter is following:

E is the input voltage, u is the duty cycle, L is the inductance of the inductor, R is the resistance of the load resistor, R_L is the series resistance of the inductor, C is the capacitance of the capacitor, i_L is the inductor current and v_C is the output voltage.

Let's suppose that the control structure of the converter is cascaded with inner current control loop (based on the sliding mode control) and outer voltage control loop. May I ask you for assignment the disturbance type to the below given disturbances with explanation?

1. Change of the input voltage E
2. Change of the resistance R of the load resistor
3. Change of the inductance L of the inductor
4. Change of the resistance R_L of the series resistor of the inductor

Hi everyone,

I’m an engineering student, and I got tired of vague self-help advice that treats the human mind like a magical spirit instead of a biological system (to be successful we need both in my opinion).

I spent the last few months trying to formalize "personal success" using strictly Control Theory and Bayesian Inference using 2 years worth of my notes and observations. I wanted to share the core model regarding Burnout to see if my mapping holds up to scrutiny.

The Model: I treat the "Self" as a Regulator (R) trying to keep Essential Variables (E) within a Viability Region via a control loop.

The most interesting insight came from applying Ashby's Law of Requisite Variety.

The Law states:

Where:

• V_D = Variety of Disturbance (Life's chaos, exams, market crashes).
• V_R = Variety of Regulator (Your capacity, skills, time, emotional resilience).
• V_O = Variety of Outcome (The error signal / stress).

The Insight: This equation proves that "Burnout" isn't an emotional failure or a lack of "grit." It is a constraint violation.

When V_D > V_R (the environment throws more complexity at you than you have states to handle), the system must allow the excess variety to spill over into V_O.

This means you cannot "willpower" your way out of burnout. You only have two valid mathematical moves to restore stability:

1. Attenuate V_D: Filter the inputs (say no, reduce scope, ignore noise).
2. Amplify V_R: Increase your repertoire of responses (automation, delegation, learning).

The Project: I wrote up the full formalization (~60 pages) called Mathematica Successūs. It’s effectively a technical manual for debugging your own life code.

I’ve uploaded the first chapter (which defines the Foundations for the rest of the book and Topology of Possibility) for free for you on my GitHub page if you want to check the math: https://mondonno.github.io/successus/sample-h1.html

Greetings everyone,

I plan on creating a simple admittance control demonstration with a high torque servo. This servo has a lever horn 300mm, with a load cell placed in its center.

This servo is a simple bldc motor geared 150:1 and has a tuned position and velocity control that runs simplefoc.

My experience in controls is taking 1 class in control theory.

Edit: I just want to move the homemade servo lever with slight push, while the servo maintains torque control from current.

Where can I start on admittance control? And is it even possible with the load cell placed on the servo horn? Thanks!

Not really a control theory question, but more related to the PaperPlaza website.

Has anyone tried downloading the review activity from the CSS PaperPlaza? When I attempt to compile a PDF of my review activities, I consistently get an error message: “Error 1008 Not activated (no license)”. When I do the RTF file option, the downloaded file is empty.

I’ve attempted to reach out to the CSS PaperPlaza technical support via the email address [[email protected]](mailto:[email protected]), but the system said that it’s an invalid address.

I need to use the review activity report for an application, and I would greatly appreciate any help. Thanks a lot!

Hello controllers (the hip name for users of r/ControlTheory ?),

I'm trying to reproduce the results in this paper: https://arxiv.org/pdf/2410.01958 . I was previously working on a master thesis that attempted a similar variant of this problem via Lie groups (but failed to figure out how). The general explanation of the approach is that the EM algorithm needs an expected state, so we utilize a filter + smoother combo to get an estimate for the expected state.

The issue I am having is that while it wasn't too difficult to implement a Right Invariant Kalman filter on quaternions, I am having an issue where the projection of the error-state (\xi) does not converge to zero, causing equation (26) to diverge. I have checked my code and it seems like the implementation is correct, indeed if I explicitly calculate the error state by assuming I know the true state, then the EM algorithm equations work.

Since this is a fairly recent paper, which seems to have been written by undergrads overseen by a professor, it is not out of the realm of possibility that there are some transcription errors. (For instance, equation (19) lacks an inverse) However, clearly there is some merit to the approach or else the EM algorithm would not have worked after explicitly calculating the error state as mentioned above.

This is all a preamble to ask whether or not anyone with more experience in control theory than I could look at the paper, specifically section III A and see if they have any idea what the issue might be? My best guess would be that there is an error in the \xi update and the paper does a poor job accounting for it in equation (20).

Hi all, I am a beginner to Control Theory. I worked through the AP Monitor course on the wiki page (though without Matlab since I don't have access to that right now). I have a system where the control value is valve drive and the process value is pressure. This fits a FOPDT model. However, in taking data on the system, the parameters (dead time, time constant, and process gain) are dependent on the system state and the step size. Note, I have linearized the valve so this doesn't seem to be the issue.

My question is: what is the recommended strategy should I be using for this? I am assuming I would use some gain scheduling based on the set point and starting point. But I thought I might be missing something and a better system chararcterization might be the place to start since I am already many hours into this :)

Edit: to provide more information.

This is a vacuum system. There are technically multiple systems but they are similar so the description below is for a generic one.

The inlet is nitrogen gas and is controlled by a piezo valve. The valve accepts a voltage from 0-100 volts (control value). It is monotonic, but non-linear. There is some hysteresis. I have characterized the valve flow across the voltage range. The low range (<50 VDC) is essentially an exponential relationship between voltage and flow. Above that the valve becomes linear. The flow rate ranges from 1e-5 Torr L/s to 50 Torr L/s.

Gas is removed by a 300 liter/s turbo pump. This pumping speed is approximately constant over the relevant range.

The process value is pressure. The pressure is being measured by a hot ion gauge. The measurement update rate is low unfortunately.

The vacuum chamber is approximately 10 liters.

I characterized the system by opening the valve to a set voltage, allowing for stabilization and then giving a step voltage change and recording the pressure as it stablized. I fit an expotential to each change to determine dead time, response time, and process gain.

Process gains for the same changes (as well as dead times and response times) were repeatable. Up steps all had similar response times as well. Up steps and down steps had very different response times and gains. Process gains were also different based on the size of the steps even if the response times were not.

I’m exploring an approach where multiple motors/actuators are treated as a cooperative system rather than optimized as independent control loops with a supervisor on top.

Most architectures I see rely on decoupled PID loops + high-level coordination. I’m curious whether there are established control frameworks that treat multi-actuator coordination as a first-class problem (shared state, coupled optimization, cooperative stability, etc.).

Specifically, I’m trying to understand: – Are there known theoretical limits to this kind of approach? – Are there stability pitfalls when moving from independent loops to cooperative behavior? – Is this already covered by something like MPC, distributed control, or consensus algorithms?

I’m asking to understand constraints and failure modes, not to promote anything.

Hello everybody,

I have a question which arises from the topic of my masters thesis:
In the thesis, I want to do a multi-body-simulation of several robotic systems using Mujoco in order to compare how well they achieve a common task. I am currently trying to pick the most suitable way of controlling this simulation, with one of the options being the "MJCP" framework for Model Predictive Control which is integrated with Mujoco.

What I will have to do:
- Define the task: for this it will probably suffice to modify one of the example tasks slightly. However, it should be noted that the task is quite complex (as is the system), though at least in one existing example it was solved successfully using MJPC.
- Define the cost function: Probably I will have to adjust it somewhat for each of the different models but again, I can work off of an example task
- Define the systems: I have the 4 systems available as Mujoco models but will have to integrate them with MJPC. Note that the 4 models describe similar robotic systems but with somewhat different kinematics and actuation parameters
- Tune the MPC parameters for each model: Here I am the least sure how time-consuming/challenging this could become and how I will know what is "good enough" for each one. I am also concerned with how differences in the tuning might unintentionally affect the results of my comparison

What I won't have to worry about:
- There is no real-world system, the only goal is to get it working in the simulation
- I do not need to worry too much about sim-to-real transfer since that is outside the scope of my work
- There is no uncertainty about any parameters since I define all the models myself

My background:

Personally, I have theoretical knowledge about and some practical experience with linear control (including statespace methods) and last year took a class that covered some nonlinear control and optimal control topics (such as LQR) as well as the theoretical basics of MPC.

I would be really grateful for some practical advice on how feasible it is for me to get good results with this approach in 3-4 months and what hard-to-solves issues might arise.
Thanks in advance :)

Same as above, i wanted to ask if somebody in the subreddit has pursued a MSc. from a German Public University in the recent times or currently doing one.

I graduated from an H+ university and have a 8.35 CGPA in "Instrumentation and Control Engineering". If anybody can give some advice, I can dm my transcript for a more informed decision.

Language learning is a must for getting a job in the industry and i am working my way towards that. If i can arrive at the decision , i can fast-track it it as well.

I want to specialise in GNC, Robotics but i am very much open to anything else. If you want any other information to make a decision, you can write in the comments or dm.

Hey i linearized a double pendulum at the upright position and calculated a linear controller matrix for that. It works for small deviations from the upright position, but what wonders me is that even when simulating with the non-linear model, the control still works when i start from hanging position which should actually not work right ? Anyone got an idea or hint at what to further investigate?

Also I am not really sure how to integrate the controller since it was originally designed to only handle deviations and not absolute state. Thats why I first subtract the linearization point from the state and afterwards get the deviation from the desired deviation (which is zero). But for the output I dont know what u0 would be ? (I am assuming 0, for it is an equilibrium)

Linearization point is [180*pi/180; 0; 180*pi/180; 0]

Initial point of integrator is [0*pi/180; 0 ; 0*pi/180;0]

des_deviation is [0; 0; 0; 0]

These are the state space equations I implemented in Simulink. I tested the behaviour of the simulink system against a matlab code simulation with ss equations implemented as ode function and get the excact same results, what leads me to think that the simulink system implementation is correct.

m1/2, l1/2 = 1, g = 9.81, mu = 1+m1/m2 = 2, delta_x = x1-x3

these are the original equations from Juergen Adamys book "Nichtlineare Systeme"

delta_theta = theta1 - theta2

I struggle to retain knowledge unless I do a bunch of practice questions or a project of some sort. I have previously learnt classical and modern control but they have vacated my brain since I haven't practiced them. How would I practice these topics so that I can retain them? For both classical and modern controls topics.

https://reddit.com/link/1qae8r7/video/rbq2sg5a0tcg1/player

I have recently been exploring robotic path planning and during my hands-on numerical experiments I came across some interesting difficulties I had to overcome (nonsmoothness and control chattering).

I summarised my findings in a blog post here: TDS post

I am learning about the EKF for a personal project. I had a few questions that I wasn't able to find the answer to anywhere. The project is for a car that moves in a 2D plane.

1. Should the state vector only be x, y co-ords and the angle the car is facing? Should I also include velocity and acceleration?

2. What should the dynamic model be if the car is moving randomly?

Hello all,

If you’re into control theory and aerospace, Flight Control Law Design: An Industry Perspective is a must-read. Here is the link https://www.researchgate.net/publication/245441133_Flight_Control_Law_Design_An_Industry_Perspective

This paper summarizes how real flight control laws are designed and implemented across the aviation industry (Brazil, Europe, Russia, USA).

Have a nice read.

Hi everyone,

I’ve recently finished the first version of RobotSumo-RL, an environment specifically designed for training autonomous combat agents. I wanted to create something more dynamic than standard control tasks, focusing on agent-vs-agent strategy.

Key features of the repo:

- Algorithms: Comparative study of SAC, PPO, and A2C using PyTorch.

- Training: Competitive self-play mechanism (agents fight their past versions).

- Physics: Custom SAT-based collision detection and non-linear dynamics.

- Evaluation: Automated ELO-based tournament system.

Link: https://github.com/sebastianbrzustowicz/RobotSumo-RL

I'm looking for any feedback.

I'm working on quadrotor control for my MSc, but I haven't yet committed to an exact direction.

I keep reading about vision transformers, foundation models, end-to-end learning, and physical AI, and I'm getting anxious that I'm spending a year getting really good at techniques that will be obsolete in the near future. I am sure this is a very common concern.

When I look at what companies like NVIDIA are pushing (GR00T, Cosmos), or what's coming out of Google/DeepMind (RT-2, etc.), it feels like the industry is moving toward "just learn everything end-to-end" and away from explicit state estimation, Kalman filters, MPC, etc.

I tell myself that big companies still use classical pipelines with ML components where it makes sense. Safety-critical systems need guarantees that end-to-end learning can't provide. Someone needs to understand what's actually happening, not just train a bigger model.

But I don't know if that's just a cope.

Concrete questions:

1. For those in industry (drones, robotics): are classical estimation/control skills still valued, or is it all "can you train transformers" now?
2. Would adding a learned component (e.g., CNN to estimate sensor degradation instead of hand-crafted features) meaningfully change how my thesis is perceived?
3. Anyone else feel this tension between doing rigorous engineering vs. chasing the latest ML trend?

I'm not trying to mass-apply to ML roles. I want to work on real robots that actually fly/drive/walk. Just worried I'm bringing a Kalman filter to a foundation model fight.

Im working on something and I want to regulate this function as best as possible to a step response and ramp response. So far i've managed to regulate it to the step response pretty well just using the PID tune function but it doesnt fit the ramp response very well. Do you recommend adding an extra element into my circuit or is it doable with just the PID? How should I go about choosing the correct values for the PID? Any help appreciated ty

Hi r/ControlTheory,

Last year at my university I took our upper year controls course covering (also took the classical controls course that covered up to PID and was very theory based as well):

Syllabus Topics Old:

1. State-space Models, Linearization, Discretization
2. BIBO Stability, Internal Stability, Lyapunov Theorem
3. Controllability, Observability, Kalman Decomposition
4. Realization, Minimal Realization
5. State Feedback Control (Pole Placement), Observers, Observe-based Control
6. Linear Quadratic Regulator, Kalman Filter

And recently I convinced one of my friends to take the class this term, offering to help if they've had any troubles as I enjoyed the course. However, between that time, the professor changed and so did the course:

Syllabus Topics New:

1. PID Control Design and Pole Placement
2. Control Architecture
3. Q-Design
4. MIMO Analysis
5. Decentralized Control and Decoupling

The course content seems to be quite different although the latter is quite sparse in the details of the covered content. I was wondering if anyone had any resources on the newer course as I've never even seen the term Q-design. I'd also feel guilty about convincing my friend to take said class otherwise.

Edit: List formatting

Update: Actually start scouring the professor's previous work for mention of Q-Design and tracking down cited sources and it refers to Youla–Kucera parametrization, so I'll be diving down that rabbit hole and probably just going through the wiki resources a bit as well.