Hi, as you said the research in control related topics is huge and the selection of a research topic in that field might depend more on your expertise. I suggest you take a look at some of the most relevant journals publishing control related papers. Some of them could be:

https://www.journals.elsevier.com/automatica

https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=9

https://www.journals.elsevier.com/journal-of-process-control

https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5488303

https://www.journals.elsevier.com/annual-reviews-in-control

https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=87

https://www.journals.elsevier.com/systems-and-control-letters

https://onlinelibrary.wiley.com/journal/10991239

https://www.journals.elsevier.com/journal-of-the-franklin-institute

https://www.journals.elsevier.com/control-engineering-practice

https://www.siam.org/publications/journals/siam-journal-on-control-and-optimization-sicon

Because I know the amount of information could easily overwhelm you, then it would be a good idea too to take a look at some of the researchers I consider are worldwide recognized in the control field. Some of them are:

Moritz Diehl

Jean-Michel Coron

Jürgen Pannek

Saša V. Raković

Sigurd Skogestad

K.J. Åström

Frank Allgöwer

Sebastian Engell

D.Q. Mayne

Tore Hägglund

Hope this information could help.

Please check publications of

https://scholar.google.com/citations?user=TcREpMQAAAAJ&hl=en

https://scholar.google.co.in/citations?user=52mcTMgAAAAJ&hl=en

https://scholar.google.co.in/citations?user=Va1aENIAAAAJ&hl=en

https://www.ljll.math.upmc.fr/coron/publienglish.html

https://snac2019.com/

All the best!

I didn't know all of those were accessible. I already knew the name of Slotine but not Talole, I will have a look, hoping to find something I can work on.

Thanks :)

Hi, as you said the research in control related topics is huge and the selection of a research topic in that field might depend more on your expertise. I suggest you take a look at some of the most relevant journals publishing control related papers. Some of them could be:

https://www.journals.elsevier.com/automatica

https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=9

https://www.journals.elsevier.com/journal-of-process-control

https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5488303

https://www.journals.elsevier.com/annual-reviews-in-control

https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=87

https://www.journals.elsevier.com/systems-and-control-letters

https://onlinelibrary.wiley.com/journal/10991239

https://www.journals.elsevier.com/journal-of-the-franklin-institute

https://www.journals.elsevier.com/control-engineering-practice

https://www.siam.org/publications/journals/siam-journal-on-control-and-optimization-sicon

Because I know the amount of information could easily overwhelm you, then it would be a good idea too to take a look at some of the researchers I consider are worldwide recognized in the control field. Some of them are:

Moritz Diehl

Jean-Michel Coron

Jürgen Pannek

Saša V. Raković

Sigurd Skogestad

K.J. Åström

Frank Allgöwer

Sebastian Engell

D.Q. Mayne

Tore Hägglund

Hope this information could help.

All these field are good to do research. But I suggest you can design a control technique which is robust as well as adaptive. For your reference you can go through my papers.

I suggest focusing on digital design methods.Very few controls are built using analog components nowadays, yet most researchers still prefer to derive digital controls by discretizing analog designs.

I would like to suggest you undertake your research in the networked control system, Consensus control or reinforcement learning as future of control system is marching towards this.

I would look into model-free (purely data-driven) methods. Model-driven approaches have been studied for years, and gotten overly complex, especially with nonlinearity or when adaptation is desired. Applications where empirically-fit models would currently be used are especially likely candidates, as are cases when iteration must be avoided to achieve tight timing requirements.

As an example, see a description of (my own) work at https://gregstanleyandassociates.com/whitepapers/BDAC/bdac.htm

The focus is on learning & maintaining a filtered/clustered data set, and then using approximate pattern matching during estimation and control. Essentially, estimation and control are reduced to interpolation between example cases. The method eliminates separate computations for model building or parameter fitting, state estimation like Kalman filtering, and control. It also avoids re-doing everything when adaptation is needed.

There is still a major theoretical need (PhD topic) -- establishing sufficient conditions for stability. A likely approach includes setting conditions for entries in the data set, and maybe more caution when extrapolating outside the convex hull of existing example cases.

Recently a big sample of control engineers has just started working in the field of modelling the dynamics and control of the pandemic. Like modelling and control of SARS-COV-2 (COVID-19).

When you check the general literature, industrial processes are mostly solved by conventional controllers. You can find a huge literature on robust, adaptive, model-free and etc control methods that are mostly plausable. Instead of conventional controllers, the machine learning based controllers are more preferable to thhink since unknown distributed systems with many constraints have more complex tasks than before. Old but powerful methods such as reinforcement and so deep reinforcement learning methods are very good field to study nowadays...

Reinforcement learning based control systems are really hot right now. Like other controllers, this is not a new methodology, but they is a ton of research on this now since the computers are powerful enough to handle it. Adaptive controller and Model predictive controllers are also very hot.

All these field are good to do research. But I suggest you can design a control technique which is robust. For your reference you can go through my papers.

Hi,

I think the best answer for this question is the aim of control study.

All these areas are suitable for a large number of applications, for exapmle if you are interested in Process Control you can study about MPC and other complex methods, but for the areas for identification and modelling stuff you can study on learning based control topics.

But the basic part is determining the application area.

Hope you can decide and study that topic.

Dear Antoine,

this is an interesting document on RG

https://www.researchgate.net/post/Anybody_suggest_me_latest_research_in_control_system_for_PhD_work

I suggest you can design a control technique which is robust.