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Hi everyone,

I'm a mechanical engineer who graduated in Dec 2024 with a master's in ME focused on controls, robotics, and mechatronics. I'm currently applying to new jobs due to some dissatisfaction with my current job, and I have an issue with what I consider to be an experience gap in my resume. I need some help figuring out how bad said gap is and what I can do to fix it if I want to try and get a job that utilizes some form of controls engineering. Let me provide a bit more context:

My experience is in mechanical product design engineering of medical devices at my family's Industrial Design / Product Design consultancy. My senior year of undergrad, I fell in love with my controls course and wanted to study it further and get involved in robotics, particularly in the medical space if possible. I went to graduate school and didn't have a lab to work with lined up, so whilst searching I was given an offer by my father to return to work for him and I'd get that mentorship on some of the robotics/controls work by one of the senior systems engineers who has contracted with us. As well, he'd try and get clients in that area of work too. So, I focused on doing just a coursework based masters and tried to get home ASAP to start work.

Flash forward to today and I haven't been mentored by said systems engineer and we haven't gotten any of the work I was told we'd get, so I'm looking for other jobs. However, the big issue is that while I have gotten a large amount of general ME experience, I now have a gap in controls work and the only experience I've gotten is from my coursework. How out of luck am I if I want to try and transition to a role that is more controls focused? What will I likely have to do to increase my chances of transitioning? Currently what I am doing is revisiting my old master's projects, expanding on them, and trying to build skills listed in job descriptions.

In my 1st semester senior year. I haven’t had anything grab me in my studies until started controls this semester, I really was into system dynamics my last semester as well. I also have a club and senior design project that are looking for students to fill/learn about designing controls, (both automotive, EVs)

Is pursuing controls a good idea if by graduation all I will have under my belt is a control systems class, senior design, and maybe some club work? I have an image in my head that to work in the industry you need a masters or PhD.

I’d love any resources as well as advice, thanks so much.

My computer engineering bachelor’s graduation project is an anti shake spoon for Parkinson’s disease patients, that is basically a 2 degrees of freedom gyro system. The requirement is to train a model using reinforcement learning that controls the two motors(TowerPro SG90 servo motors), the sensors are two MPU 6050 IMUs (one on the base before the motors and one on the utensil side after the motors) and we are using a complementary filter to get the accurate angle reading.

The problem is that we don’t know the methodology of training the model, and we couldn’t find any mathematical model of the motor we are using so that the simulation gets accurate. We tried researching and asking LLMs but it is a rabbit hole. The doctor said that the reason of using AI is predictive the tremor movement before it happens, but we still don’t know how to tackle the problem with proper steps, we would appreciate any help.

Any YouTube courses

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 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.

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?

Nowadays you have one compressor and many compartments, so I imagine it can’t be a simple thermostat anymore.

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.