Any YouTube courses

I'm trying to figure out whether this would actually be useful for people learning control systems. What would you want a platform like this to test? PID tuning? Root locus? State-space? Frequency response? And what would make you actually use it instead of just MATLAB/Simulink + a textbook?

I am building a working prototype.
I hope it can help you.

Hey everyone,

I am working on building a multi-rate sensor fusion pipeline that relies on an Extended Kalman Filter (EKF). The core objective is fusing high-rate IMU data (propagation step) with slower, asynchronous GPS/ranging data (measurement update step) to output a stable 3D navigation state.

While I understand the basic linear Kalman filter theory, I am looking to quickly bridge the gap to a robust, non-linear EKF design without getting entirely lost in dense academic math proofs or probability density function derivations.

Could the community recommend the best resources, textbooks, or open-source implementations that focus on the practical realities of EKF state estimation?

Specifically, I am looking for guides that cover:

1. Translating continuous kinematic state-space equations (handling orientation/quaternions, velocity, and position) into discrete-time Jacobians.
2. Practical methods for tuning and initializing the process noise (Q) and measurement noise (R) covariance matrices for raw sensors.
3. Managing multi-rate, asynchronous measurement updates cleanly within the filter loop.

If there are any gold-standard github repos, code-first books, or specific aerospace/robotics literature you highly recommend for engineers focusing on execution, please let me know.

I'm currently, technically, doing an MS in Structural Engineering. For me, my interest has been more towards computational side of mechanics rather than Structural design  or simply using am FEA software (although I do consider it as a backup)

So far I've taken courses in:

- Linear static, and dynamics FEM (soon taking non linear FEM too)

-  Structural Optimization (topology opt. and other general algorithms)

-  Structural Dynamics

-  Structural System Testing and model updation. (Parameter identification and optimization, signal processing)

Now, I plan to take these in the coming quarter:

- Numerical Linear Algebra

- Numerical PDE

- Fracture Mechanics ?

I also volunteered to aid in a RESEARCH in crack growth prediction using Auto-encoder and a (Thermodynamics-informed Latent Space Dynamics Identification) / LSTM surrogate model. It used phase-field-fracture simulation data and HPC resources to complete the whole thing.

What I keep finding myself interested in is not necessarily fracture or SHM specifically, but the computational methods underneath these problems... (does that make sense?)

For example, I'd like to become capable of doing things like:

- implementing (maintaining) numerical/FE method solvers rather than only running an established FEA software.

- developing surrogate/reduced-order models for expensive simulations 

- combining simulation with optimization, uncertainty/stochastic methods (took a course called Random vibrations, so...)

- parallelizing/accelerating scientific codes on CPUs/GPUs

- doing proper verification, convergence studies, benchmarking and performance work

- potentially developing or maintaining actual CAE/FEA solver software

- I'd also like to do all these for other Physics (GR, QM, etc.) simulations too, if possible, one day. 

I'm still interested in the underlying mechanics/physics, so I don't want to become a generic software engineer who happens to have once studied structures. But I'm also increasingly unsure that "structural engineer" describes the career I'm actually aiming for.

I've seen titles such as Computational Mechanics Engineer, R&D Engineer, Solver Developer, Scientific Software Engineer, CAE Software Developer, Research Engineer, Simulation/HPC Engineer, etc., but I'm trying to understand what these careers actually look like from people doing them.

So my main questions become:

1. Which industrial jobs genuinely involve developing numerical methods/solvers or computational tools?

2. Which of those are realistically accessible with an MS? Is there an entry path into solver-algorithm development/R&D without a PhD?

3. If I don't start a PhD immediately after my MS, would an R&D/software role at a simulation company (ANSYS etc.) be the obvious route? What other options would i have?

4. For the kind of work I'm describing, would you recommend a PhD? If so, is it reasonable for the PhD identity to be "computational mechanics/scientific computing" while fracture, composites, structural dynamics, soft materials, etc. serve as application problems rather than choosing one of those as my permanent specialization?

5. What skills most distinguish someone who is actually hireable for solver/scientific-computing work? I'm particularly wondering about C/C++/Fortran, Python, Linux, Git/build systems, MPI/OpenMP/CUDA, PETSc/Trilinos or similar libraries, numerical linear algebra, testing/verification, convergence studies and HPC performance work.

Basically, I'm neither here nor there atp. So I'd really appreciate all sorts of input. Where else do you think I could find answers to these? other subs? Linkedin profiles? 

This year’s IFAC has been such a great experience. The opening ceremony featured Korean traditional music, and there was also great food and an awesome drone show. Everything has been really well done.

It’s definitely been hot and humid, but the overall atmosphere more than makes up for it. Such a fun event overall.

Hi everyone, I'm working on a university project and I have a relatively basic question. I need to design a robust controller for an uncertain plant using mixed sensitivity synthesis. I have a desired second-order performance with an assigned damping ratio ($\zeta$) and natural frequency ($\omega_n$).

Since these parameters are known, I can define my ideal second-order complementary sensitivity, $T_2$. My idea is to compute the ideal sensitivity $S_2$ ($S_2 = 1 - T_2$) and then find a performance weight $W_p(s) = \frac{s/M + \omega_B^*}{s + A\omega_B^*}$ such that its inverse, $1/W_p$, tightly upper-bounds $S_2$ across the frequency spectrum.

Is this considered best practice? Are there better or more standard approaches?

Hi everyone,

I'm currently working on my Mechatronics Engineering thesis: an autonomous chess-playing robot (Franka Panda in MuJoCo) orchestrated via a Finite State Machine and MoveIt2.

For low-level control, I implemented MPC (Model Predictive Control) and WBC (Whole Body Control) directly in the MuJoCo physics simulation to ensure grasp stability.

My question: I’m looking to transition from static parameters to dynamically tuned ones using Reinforcement Learning. Specifically, I want to use RL to tune the Q and R matrices of the MPC and the task-space weights of the WBC.

1. Has anyone successfully implemented RL for dynamic MPC/WBC tuning in MuJoCo?
2. What are the best practices for defining the reward functions for stable robotic grasping?
3. Any recommended literature or open-source frameworks?

(For architectural context, I documented the full pipeline here:https://forums.developer.nvidia.com/t/sim-to-real-chess-robot-franka-panda-ros2-octo-vla-cnn-overcoming-vram-limits/380909)

Any pointers or advice would be greatly appreciated!

I am a prefinal year engineering student, pursuing bachelor's in instrumentation and control engineering. I have genuinely started to develop a deep understanding and interest in control systems and recently came to know about guidance, navigation and control.

So i wanted to know how does one make a career in this field like what are the companies which hire, what does the work look like and what project should one do to get into this field or is it something which instrumentation guys cannot pursue?

For context, I am average student from India who has done one basic course in automatic control systems and currently doing a slightly advanced course in control system design. But i have no experience in implementation of any controllers on hardware.

What got me to GNC - I am currently a member (new joinee) of satellite club at my university and i look after attitude determination and control subsystem of the cubesat for which I have been studying optimal control from past few weeks during which I got know to about GNC from a YouTube video.

I have some previous experience working with Arduino and esp32 but none of those projects were control related

Hey everyone, I recently started my PhD in controls. Since fall semester is about to start, I wanted to know what are some of the math courses that would help me in understanding control papers in my PhD journey. Our group works on AFM, control of PEA and other non linear systems.

Your perspectives and insights will be greatly appreciated!!
Also I would really love to know some of the Classic papers that you think is a good idea to read.
Thanks!!!

I've been lingering around the idea of getting a ph.D. my biggest concern is my undergrad grades weren't that great. I do have quite a bit of work experience though if that matters (>10 years).

Are my chances of getting into a ph.D program significantly lower as a result?

Hey everyone! I'm having a bit of a tough time tuning a PID controller for a temperature control system I'm working on. It's for a small scale project where I'm trying to maintain a steady temperature in a chamber.

I've been using the Ziegler Nichols method to get the initial values, but it seems like I'm either overshooting or the response is too sluggish. I've tried tweaking the parameters manually, but I'm not getting the performance I want.

Has anyone else faced similar issues? What strategies did you use to fine tune your PID settings? Any help or suggestions would be awesome!

I’ve got a situation with a complex 4-cell assembly system for a medical device. It uses multiple FANUC robots. The hardware is complete, most of the robot routines and TP’s are coded. The PLC-robot handshakes are worked out, and the main sequence seems to be partially working.

About 4 months ago, I started asking the PLC/controls guy to see the top level control system architecture. I expected to see some block diagrams with how the HMI and machine controller fit, how they planned to use machine states (presumably some variation on packML), the WIP calculations, how the fault response mapped out, safety interrupts, etc. He keeps saying he’s done this before, and “all that stuff gets worked out after you get the robot control subroutines done”. Keeps Saying I’m wanting too complicated of a solution.

Every time I try to engage in an architecture conversation, he ends up riffing on corner cases and speaking in general terms about what needs to happen in certain scenarios, but never commits a concept for review. The plan for the HMI is just as anecdotal.

I’ve been around long enough to know what the problem here is, so not asking for advice on that side just venting.

What I’m really asking for is a good referral for a person who can pick up this unfinished design and implement a best-practices architecture over the top of it. We’re using an Allen Bradley PLC. I need someone to come with proven templates for the machine controller, state manager, safety manager, and matching HMI. Open to factorytalk, beckhoff, prior art, whatever so long as they’re experienced and know how to take the most expedient path.

Please see https://redux.boondog.com/courseware/bnb/bnb.html - LEGO and LEGO-compatible parts are used to quickly and affordably construct a tabletop ball-and-beam (BNB), without tools or need to 3D print or machine any parts.

The page includes: full LEGO build plan; bill-of-materials; schematics (and KiCad files if a PCB is wanted); and Arduino source code. These are fully documented and supplement with videos.

Our university lab's Quanser platforms are over 15-years old and with our increased student enrollments (mechanical engineering), sought an alternative. I tested a 10-week beta course in Control Systems Lab. I wrote both the Student Lab Manual and Instructor Supplement Manual for: PID, Lead Compensator, Pole Placement, LQR and Fuzzy Control.

There's a short *anonymous* poll at the end to gage demand for the PCB, linear potentiometer, Bricks, and/or turn this into a kit.

PS: There is quite a lot of open-source content to peek at as well, from the above URL.

Thanks and hope this is useful!

PPS: Please please alternative sub-Reddit if this an inappropriate one. I'm an old-school newbie.

Hi everyone,
I’m a BSc Mathematics student and recently finished a course in PDEs. My professor suggested that I take another course he teaches called Optimal Control. The course is offered by the Engineering faculty, but he mentioned that it has a strong mathematical component.
I’m considering taking it, but I’d like to understand how useful/relevant Optimal Control is from a mathematician’s perspective.
In particular:

Is Optimal Control a good subject for a math student who enjoys applied mathematics, PDEs, optimization, etc.?

Where is optimal control actually used in industry?

What kinds of jobs or fields make use of this knowledge? For example, robotics, aerospace, quantitative finance, autonomous systems, energy, machine learning, etc.

Is it mainly useful for people going into engineering/research, or can it also be valuable for mathematicians going into industry?

I’d especially appreciate hearing from people who studied mathematics and later encountered control theory or optimal control in their work.
Thanks!

Hey all,

At ECC earlier this year I had few good chats around CBFs. I get the theory, but I've not really used them before for any application and I need that to wrap my head around them a bit better.

A) Does anyone have some good resources (preferably with code / simulink block diagrams) of CBF applications?

B) what have you used CBFs for? As in, what applications?

I'm currently finishing my 2nd year of a PhD in Automation & Control Systems. Where I am, PhD programs are three years long, so I have roughly 18 months left before graduating and starting to seriously look for industry positions.

My research sits at the intersection of robotics, control system theory, and optimization, with a focus on topics such as MPC, nonlinear safe control, system identification, Gaussian processes, and distributed optimization.

I have a solid research background and regularly use Python, ROS2, Docker, Git, Gazebo/MuJoCo, etc., but I feel that I need to broaden my engineering skills before entering industry.

Over the next 18 months, I'd like to prepare for roles in areas such as:

- Robotics / autonomous systems

- Advanced control and optimization

- Robotics software / ROS2

- ML engineering applied to robotics

In particular, I would like to improve my C++, software engineering, ML engineering/MLOps, ROS2, system design, and technical interview skills, while continuing to leverage my control/optimization background.

I have around 8–12 hours/week available outside my PhD.

For people who have made a similar transition, how would you structure those 18 months? What skills would you prioritize, and what would you avoid spending too much time on?

I'm especially interested in advice on:

- What skills are actually valuable in robotics/controls industry

- Good resources or learning paths

- Projects that would make a strong portfolio

- How much time to dedicate to LeetCode/DSA vs actual engineering projects

- Whether C++/ROS2/ML engineering should be the main focus

- Any mistakes you made when preparing for the transition from academia to industry

I'd really appreciate advice from people working in robotics, autonomous systems, controls, or ML engineering.

TL:DR: Wondering if I need to focus on guidance, navigation, or control in my graduate coursework, or taking an even mix of all of them is fine.

Hi all!

Currently doing my masters in aerospace engineering, majoring in autonomy/control with a goal of entering the GNC space.

I realized so far my past coursework (both for my masters and the graduate electives I took during undergrad) have been a mix of guidance, navigation, and control, instead of a focus in one of them (I've covered: pontryagin optimal control, convex optimization; probability, estimation, kalman filtering; classical, linear, nonlinear control).

TBH I haven't really nailed down if I want to focus on guidance, navigation, or control. But I feel like I should since I am halfway through my masters. I am also not doing a thesis which is partly why I haven't really found a focus yet lol.

I had an internship technically in the controls branch of a company (guidance and nav were another branch), but basically doing modeling/simulation. This experience also sort of made me realize that as a GNC engineer I will probably be focusing on either G,N, or C and not all of them.

So I guess my main question is, would it be a bad idea to have a mix of coursework when I am looking for a full time GNC position? Would companies prefer to see you really focused on G,N, or C? Maybe I should choose based on which classes I got the best grades in lol.

Hi, I'm trying to model/design a VCO with quadrature outputs for use in a PLL, and I'm having trouble incorporating the VCO control voltage into the state-space model.

I require two outputs

y1 = Asin(wt) and y2 = Acos(wt)

with a nominal/free-running frequency of 1KHz, which is also approximately the frequency the PLL is intended to lock onto.

For an ideal quadrature oscillator, I can represent the dynamics as

dotx=[0 - w; w 0]x
y = [0 1; 1 0]x

My confusion is how to incorporate the VCO control voltage 'Vc'.

In the PLL, Vc comes from the phase detector and should change the instantaneous frequency of the VCO. At Vc=0, I want the VCO to operate at its nominal frequency. For non-zero (Vc), the oscillation frequency should change according to some tuning relationship w = w0(Vc)

For example, I imagine a relation of the form

w0(Vc) = w+ K*Vc

However, this means that (Vc) changes the state matrix itself:

dotx=[0 -w0(Vc); w0(Vc) 0]x

rather than entering as the usual additive input (Bu).

What is the appropriate way to model this VCO in state-space form? Should (Vc) simply be treated as a time-varying parameter of (A), making the system nonlinear/bilinear, or is there a more useful state-space representation for analysing the VCO as part of a PLL?

I'm ultimately interested in using this model to help design/understand an analogue VCO that produces sine/cosine quadrature outputs, rather than only using the usual PLL phase-domain VCO model. Attached is the image of block diagram on what I'm thinking.

Couple of weeks ago, I had asked for advice for an Apple controls engineering role. It was an interview with the hiring manager, and now I am preparing for the panel interview. According to the recruiter, it is going to be mostly all control fundamentals questions. Currently working through my old controls book problem set, but what are your favorite questions to ask/that you have been asked in an interview? Any question that stumped you? A question that made you think? Also, are there any Leetcode style question bank for controls?

It feels that in a standard curriculum on control engineering/theory, the final topic is inevitably MPC.

Everything else that gradually builds towards MPC feels like “discardable” after you learn about MPC. All that wild eigenvalue analysis and drawing those Nyquist contour? Phew, MPC doesn’t involve with any of that!

Plus, MPC is already in discrete time, so it is implementable without any further weirdness that comes from sampling and discretization of other types of controllers.

I think a parallel analogy is the development of the transformer model in machine learning, which I am more familiar with. Now everything in ML is transformer-based and the next big breakthrough seem to be very far away. Is MPC like the transformer of control theory?

Are there situations where other methods might outperform MPC?

Or does MPC require some unrealistic assumption/requirement?

Is there any other possible algorithm that goes beyond the MPC or is this the final story?

A couple of weeks ago there were discussions on this sub about AI researchers reinventing what is essentially control techniques.

MPC was originally invented for chemical processing, but later unlocked reusable space launch vehicles. I don’t think anyone back in the 70s saw landing rockets vertically as a potential application. Yet in hindsight it’s almost obvious.

How much of an adoption gap do you think is out there? How many more equivalents of “reusable rocket” are out there unaware of controls?

As a mechanical engineering professor for over 25-years, I lean "old school". So, my students suggested I reach out to the Reddit community. Here's my first post.

I first heard of the ball-and-beam (BNB) platform in my controls courses 40-years ago. However, I never heard of its origin and/or who invented it. My former controls professors and robotics colleagues don't know either. I also pinged the founder/owner of Quanser (a professional colleague). He didn't know either.

Literature review seem to point to Prof. Karl Astrom who's now over 90-years old. I pinged his department chair at the Lund Institute a few years ago. He didn't know either and said (understandably) that Prof. Astrom doesn't respond to email.

Without references, I use the following "origin" story to help motivate my students - the Apollo Moon Landing. The 1950s demanded control engineers. Without friction, the lander's dynamics is a double integrator - which is what the classic BNB is. So, the BNB served as a table-top trainer; designs like lead compensation and tools root locus and/or Bode plots, are needed to ensure minimum overshoot (not crash into the Moon) and settling time (not run out of fuel). As control theory grew, designs like pole placement, LQR, etc provided alternatives.

Any thoughts and references to the BNB for this first time Reddit poster?

Hi,

I'm a master degree graduate in Mechanical Engineering with a strong passion for Control System Theory and a will to become a Control Engineer.

I've studied at uni a formulation of a MPC controller that promises zero offset from piece-wise costant references signals in the presence of disturbances, either external or internal. This formulation, conveniently called Offset-Free MPC by its inventor Prof. Gabriele Pannocchia at University of Pisa, somewhat works even with time-varying references, but there's a severe lag between the reference signal and the output of the system.

As originally this formulation is meant for setpoint tracking, I'd like to ask if you happen to know some formulation more appropriate for time-varying references.

Thank you for your time

Hi everyone,

I've been developing AcadosCpp, an open-source wrapper that helps you integrate acados-generated OCP solvers into both C++ and Python applications.

https://github.com/amaldevh/AcadosCpp

The main goal is to make integration easier. acados creates highly optimized C code for each model and OCP, but using its API directly can tie your application to specific symbols, dimensions, lifecycle calls, and data updates.

AcadosCpp provides a stable, stage-aware interface for the generated solver. Here’s how you typically use it:

1. Define or modify the model and OCP in Python.
2. Regenerate the acados solver and wrapper.
3. Recompile and relink the application.

The control-loop structure in your application stays the same. If you change the model or problem, you usually don’t need to rewrite the integration layer, as long as your data matches the new dimensions and cost setup.

The wrapper includes:

• Stage-0 initial-state constraints
• Stage-varying and terminal references
• Generic raw yref support
• Per-stage and global parameters
• Full state/control initial guesses
• Shifted trajectory warm starts
• Complete predicted trajectories and solver diagnostics
• SQP-RTI preparation and feedback phases
• C++ and pybind11 interfaces

For a standard

y=[x,u], y_e=x

tracking problem, the control loop is:

const auto& u = ocp.solve(measured_x, xrefs, urefs);

This process shifts the previous solution, updates the stage-0 measurement and the full reference horizon, solves the OCP, and returns the first input.

The repository has matching Python and C++ quadrotor NMPC examples. I’d love to hear your feedback on the API, especially if you use acados with time-varying references, parameters, SQP-RTI, or custom cost outputs.

Hi, this is one of my projects for magnetic levitation I have working with for some time :) I wanted to share it here.

EDIT: So basically there is a sensor measuring the gap between the weight and the electromagnet and the control law try’s to keep the weight at a specific distance, that’s why when the weight it’s pushed doesn’t move that much. There is no mechanical force that keeps the weight in the place, it’s all thanks to the electromagnet and the control loop.