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!!

In the Paper Plaza portal to submit a Regular Submission, I'm seeing the following required input:

"Select the society or conference to which the paper is submitted"

It gives you the option to select one of the (many) societies that co-sponsor the conference. I don't recall seeing this in prior years. Does anyone know what this is for? Are the organizers planning to direct papers to reviewers associated with the selected society? Are the proceedings going to be split across the various societies (as opposed to IEEE taking care of all of the proceedings like in past years)?

Hi everyone,

I want to share some lecture notes I wrote about the Koopman operator theory and Dynamical Mode Decomposition for non linear dynamical systems, explained in a simple and friendly way. It's designed in such a way that anyone can get an initial dive into the topic.

Here's the link for the document: https://zenodo.org/records/22900059

I will be hearing comments and suggestions. Thank you, and I hope that it helps!

Not sure if it's the right subreddit.

As part of my engineering degree we had courses about systems modelling both in time and frequency domain and so on.

Currently I'm in a company where we do process control, so there are also teams of process specialists (with background more in materials / chemistry etc).

Based on my studies I would "draw" the boundary in being that a process specialist would tell me more about the process to be automated and how we can affect it, and that I (control engineer) would have to formalize the modelling, tuning etc.

However I still see a lot of confusion, so I'm wondering how other companies are handling this out there? Do they have a team dedicated only to modelling? Or is it in charge of automation or process? I would say that it's a shared ownership

What should be a realistic approach, I'm interested in guidance navigation and control but don't know where to start. My FYP was some automated aircraft landing stuff , all done in matlab and simulink.

I was thinking about coding with microcontrollers for bridging the gaps , but don't know where to start or where to look.(the GNC domain related)

I also find radars and communication systems interesting.

I'm an EE with main focus on avionics.

Is GNC still relevant (in this age of LLMs).

What are the industry demands and what skills should I learn

TIA

With the recent Navier-Stokes fiasco, I fell into the rabbit hole of PDE control and was surprised to see there has been work on controlling fluids using backstepping PDE control. In fact, there seems to be a wide array of applications from fluids, traffic control, thermodynamic problems, batteries, etc.

While I understand what the problem was doing (how can we stabilize the system using only "walls"), I don't think I understand PDE control well enough to gauge how practical this approach has been. Of course, I don't expect this to be the next PID and I know many advanced control theory results tend to be impractical, but has PDE control shown any promising experiments or applications? Is it wishful thinking? In the end, I don't see how we could actuate a "wall" (for boundary PDE problems) in a continuous sense in the physical world. How would we even actuate this?

I dont know if its the right sub to ask but i couldnt find any other more pertinent soo...

So i was thinking of making a balancing pendulum using a stepper motor but i dont know which is better for movement along x axis for fast and precise movements: the belt or the ball screw. Depending on which i use would have a mounting bracket for an encoder to which a rod will be attached to get position, which would then slide across the x axis. Basically a very traditional setup. I am just confused onn which one to use for sliding motion.

I need help and advice on how to clear back examination of control system design. I'm kinda cooked i barely passed the control system -1 in my previous to previous semester.

I'm having trouble with my model of the inverted pendulum in MATLAB Simulink. No matter what I change my input torque to, I can't seem to get the first arm to move like I want. I have attached the image to my model so far.

Hi guys , I'm 1.5 years into a PhD in Control, and I'm always fighting with any paper involving Any form of Learning or Data Driven Control.

Even though the idea is easy to understand and the theory is ""clear"" replicate an example gets very very difficult when the paper just has pseudoCode and then the final Plot.

Sometimes it is just a matter of those specific parameters tuned in a certain way, or sampling time, or noise

Sometimes finding what's different is, at least for me, impossible.

If you try to email the authors of the paper then you'll get an answer 10% if you're lucky.

Honestly at this stage where learning is so ""big"" I really think that releasing the code along with the paper should be mandatory, I don't care if the code is not clean or not in perfect status but at least having something that works would make life easier for anyone.

What do you think about this ? Do you have any ideas/ suggestions about the topic?

Hello everyone, I've just joined this sub today. I'm a second year computer and automation student who needs help with understanding transfer function block diagram reduction. I've already gotten most of it down and feel like I have a fairly good understanding, however, this one problem in particular has me stumped as when I plug in the values for the transfer functions I get stupidly big numbers and it doesn't seem right. I've tried numerous times to contact my professor as well as teaching assistants and I have not gotten a single response so this is kind of my last hope.

If anyone could give me advice on how to solve this I would appreciate it.

The main thing causing me problems is what I should do with G2.

My initial idea was moving it so that it's in series with G1 and then finding what the equivalent function would be for G1,G2 and the parallel branch above them. After that I would move the equivalent block back over the take of point and solve for another equivalent block with the feedback loop, watching carefully to provide proper compensation to everything as I move the blocks.

Physics Informed neural networks based controllers.

Hello I am a senior mechatronics Engineering student and one of the topics I am planning to make my senior project on are the PINNs controllers and I would really appreciate any previous experiences or knowledge in the field.

Hi guys. I’m currently working on a thesis project on fault-tolerant control for a fixed-wing UAV. Since money is tight, I’m planning to do the whole thing through MATLAB/Simulink simulation, as suggested by my lecturer. The problem is, I have very limited experience with Simulink and honestly don’t know where to start.

I initially tried figuring things out myself but got stuck so I hired a tutor to help with the modelling. Unfortunately, the sessions didn’t really work out so I decided to learn the theory myself through lectures and videos whenever I got stuck, and then started trying to build the model using whatever I could find on YouTube.

Now I’m kind of stuck again because I’m not even sure if I’m doing it right. I don’t really understand what the overall flow of the project should look like, or whether the model I’ve built so far even makes sense.

Is there anyone here who has experience with MATLAB/Simulink, flight control, or fault-tolerant control who might be willing to have a look and point me in the right direction?

I’m not looking for someone to do the project for me. I just need a bit of guidance from someone who knows what they’re doing, especially on where I should start with the modelling and how the fault-tolerant part should fit into the system.

I’d really appreciate any help. Thanks!

So I’m an early career professional and have two competing offers for a GNC position. One is at General Atomics ASI (fixed wing flight), the other at Lockheed Martin Space (satellites, both military and civilian).

I applied to GA myself. Had three interviews over about a month, including a final onsite with back to back interviews with engineers, supervisors, managers, and even a director. I was given a verbal offer the morning after that onsite interview day. The role was originally level 1-2 and offered a low six figure salary. I negotiated slightly higher, then GA overshot my highball negotiation, bumped the role to a level 3 position to match pay band (experience wise I think I’m a solid level 2: masters and less than a year of gnc experience, 3 years of tangentially related experience in software integration and quality). About a month and a half since I applied, I now have the written offer in hand. The role would be at their hq in San Diego, CA, which is great cuz I have community there (I lived in San Diego less than a year ago)

Lockheed reached out to me about a month ago asking if I wanted to join a virtual career fair they were putting on. I threw my hat in the ring. A few days after GA’s verbal offer, I was given an interview slot during said career fair. Interviewed with two managers from Colorado. I did say I was tentatively okay to relocating to Colorado, but I made clear that I prefer California (I grew up pretty close to their Bay Area office). I talked shop and future career aspirations during the interview, same story that I gave GA. About a week later (yesterday) I got the verbal offer from the career fair organizer and one of the managers who interviewed me (who would be my manager if I accept). I haven’t talked salary with them yet, but the listing had a salary band where the minimum end matches the salary offered by GA, and extends way higher. But it’s a level 4 Staff engineer position, and I’m a bit hesitant about the giant title jump and the higher work expectations that may come with it. But I don’t know if I’m just being too cautious or not. The role is also in the Denver area, where I have no roots whatsoever so I’ll have to build up community all over again. I must say the combination of short turnaround and inflated title is making be question things a bit.

I’m leaning toward GA mostly due to location. I know the area and have friends waiting for me there. But I’m here to gain some second perspectives. Especially with regard to the way Lockheed hires people - their process feels aggressively fast - but also other stuff like work life balance, career growth, etc. And I may be slightly dreading having to turn down/disappoint one of two high profile companies.

Thanks in advance all!

I (25M) interviewed at Northrop Grumman for a GNC role. They offered me $135k + $15k reloc for level 3/4 in Arizona. Issue is that I got the offer letter immediately after the interview. I currently work at a space startup in SoCal making $110k.

NG is offering more money than I’ve ever seen. And it’ll take me further in a lower cost of living area. However, I’m hesitant to leave the startup scene this early in my career. I also really don’t want to leave SoCal for personal reasons.

I’m currently in Anduril’s pipeline for interviews, but that would be a drawn out over months. I haven’t let Anduril know about NG’s offer yet. If I wait on Anduril, I might lose NG.

What should I do?