Which control technique or converter topology do you need to use as a PG/Phd research scholar?

Which Control Technique?

PG/Phd research scholars of an engineering field generally have some most common questions during starting phase of their research career like,

  • Which control technique should I have to use?
  • Which converter topology I have to use? Etc.
Which control technique do I need to use as a research scholar?

What Students Asks?

Here some students misinterpret the question as well as the research goal. So let me clear some understanding before answering the question.

When it’s only about using a control strategy and their result in the thesis, the question is ok.

But not using a control strategy does not mean that you should skip the theory as a research scholar. Here is the big mistake most today’s research scholars make which results in unclarity about their work. Students think that they will have to learn only a few things that can be documented in paper and their thesis.

For example, many PG students of electrical engineering are desperately asking for someone’s help to finalize a control strategy or converter topology. Even some students want their guide to help them to finalize the best suitable control strategy.

Main Problem:

The main problem is that students come with only names of the techniques like Fuzzy Logic, ANN, DTC and more without knowing even the basic fundamentals.

Today’s students have the mentality that they will learn the topic only if it is needed for their dissertation work. This thought is stopping him from moving forward toward a true researcher.


If you are a research scholar and having a question “Which control Technique should learn” then you have to study and understand each theory that you can name. There must be a passion to learn new things. Knowing the name of a theory and don’t knowing anything about how its work is not the thing that one research scholar can handle.

Most theories use knowledge of what you have already studied so far. It will not take much time to study the concept of any new theory as compared to the time students waste in validating the usefulness of any theory without knowing anything. Some theories like Fuzzy Logic and ANN are more easy to implement as compared to understanding the mathematics behind how it works.

Giving an example for a reference, I studied Fuzzy Logic and its implementation in 2009-10. I got results of Fuzzy Logic showing better performance than conventional PID controllers for speed control of SRM Drive. But because I was focused on sensorless control rather than speed control techniques, I have neither published a single paper about it nor documented anything about fuzzy logic in my thesis. I have my paper published in Artificial Neural Network based speed control techniques used for sensorless operation of SRM.


Thus I suggest the PG/Phd research scholar to learn more first, and then decide what to use and what not for your research work. And don’t put unnecessary limits on yourself, explore more, learn more and practice more. Everything will be beneficial when you have enough knowledge and practice.

If you are a research scholar, then this is the time when you learn the most in your entire life because once you become a professional, there will be very little time that one can spend learning new things from a daily schedule.

Here I am also sharing the details of the Fuzzy Logic course that can help you to start understanding and implementing a Fuzzy Logic.

Fuzzy Logic: Quick Start Guide (MATLAB)

Learn Fuzzy Logic with hands-on MATLAB practice

Fuzzy Logic MATLAB Course

– Thank you

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By Dr. Jignesh Makwana

Dr. Jignesh Makwana, Ph.D., is an Electrical Engineering expert with over 15 years of teaching experience in subjects such as power electronics, electric drives, and control systems. Formerly an associate professor and head of the Electrical Engineering Department at Marwadi University, he now serves as a product design and development consultant for firms specializing in electric drives and power electronics.