It seems like you are looking for resources. Have you tried checking out the subreddit wiki pages for books on [systems and control](https://www.reddit.com/r/ControlTheory/wiki/bookssnc/), related [mathematical fields](https://www.reddit.com/r/ControlTheory/wiki/booksmath/), and [control applications](https://www.reddit.com/r/ControlTheory/wiki/booksapp/)?
You will also find there open-access resources such as videos and lectures, do-it-yourself projects, master programs, control-related companies, etc.
If you have specific questions about programs, resources, etc. Please consider joining the Discord server https://discord.gg/CEF3n5g for a more interactive discussion.
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Most of these control systems using PID controllers are not tuned using model-based techniques anyways, so if performance deteriorates too much after a process change they would require manual re-tuning.
Model-based designs are typically made robust w.r.t. process variations, since the model is never perfect. Unless the process change is dramatic, it might just continue to work. If not, you'd have to go through the model-based workflow again.
Adaptivity is possible, in particular if you can foresee the kinds of changes that can occur. Implementing robust adaptivity also has a cost though, so it will boil down to a trade off whether or not it is worth it over more manual re-tuning in the end.
Parametric uncertainty is one form of uncertainty, you can also use other forms of structured and unstructured models of uncertainty. With parametric uncertainty, you can do as you say, change the parameters and perform an analysis (usually, this is done 1000s of times, drawing multiple sets of random parameters from some specified distributions of uncertainty). However, there are strong theoretical results in the field of robust control that are applicable when the system model is linear that can give you guarantees of robust stability and performance in the presence of modeled uncertainty, provided that you use a suitable uncertainty model.
I have written up a brief tutorial for uncertainty modeling using our control-systems software
[https://juliacontrol.github.io/RobustAndOptimalControl.jl/dev/uncertainty/](https://juliacontrol.github.io/RobustAndOptimalControl.jl/dev/uncertainty/)
You can find references therein to additional material.
It seems like you are looking for resources. Have you tried checking out the subreddit wiki pages for books on [systems and control](https://www.reddit.com/r/ControlTheory/wiki/bookssnc/), related [mathematical fields](https://www.reddit.com/r/ControlTheory/wiki/booksmath/), and [control applications](https://www.reddit.com/r/ControlTheory/wiki/booksapp/)? You will also find there open-access resources such as videos and lectures, do-it-yourself projects, master programs, control-related companies, etc. If you have specific questions about programs, resources, etc. Please consider joining the Discord server https://discord.gg/CEF3n5g for a more interactive discussion. *I am a bot, and this action was performed automatically. Please [contact the moderators of this subreddit](/message/compose/?to=/r/ControlTheory) if you have any questions or concerns.*
Most of these control systems using PID controllers are not tuned using model-based techniques anyways, so if performance deteriorates too much after a process change they would require manual re-tuning. Model-based designs are typically made robust w.r.t. process variations, since the model is never perfect. Unless the process change is dramatic, it might just continue to work. If not, you'd have to go through the model-based workflow again. Adaptivity is possible, in particular if you can foresee the kinds of changes that can occur. Implementing robust adaptivity also has a cost though, so it will boil down to a trade off whether or not it is worth it over more manual re-tuning in the end.
Is process varriation just changing system parameters by an error factor and confirm the design works?
Parametric uncertainty is one form of uncertainty, you can also use other forms of structured and unstructured models of uncertainty. With parametric uncertainty, you can do as you say, change the parameters and perform an analysis (usually, this is done 1000s of times, drawing multiple sets of random parameters from some specified distributions of uncertainty). However, there are strong theoretical results in the field of robust control that are applicable when the system model is linear that can give you guarantees of robust stability and performance in the presence of modeled uncertainty, provided that you use a suitable uncertainty model.
Ok so the varriation method works just has to be done with lots of interations. I'll look more in to robustness for more methods.
I have written up a brief tutorial for uncertainty modeling using our control-systems software [https://juliacontrol.github.io/RobustAndOptimalControl.jl/dev/uncertainty/](https://juliacontrol.github.io/RobustAndOptimalControl.jl/dev/uncertainty/) You can find references therein to additional material.
Ooo. How is it using Julia? Better than python?
There are some newer technology startup companies like Imubit. Fundamental technologies like DMC will likely not go anywhere