Observer-Based Control of Inductive Wireless Power Transfer System Using Genetic Algorithm

Round 1

Reviewer 1 Report

Point 1: the introductory part should be more extensive and explicit.

Point 2: the literature review should be updated (only 2 titles from 2021 and only one from 2022)

Point 3: Authors should explicitly declare the limitations of the study

Point 4: The plagiarism check indicated a high degree of similarity to other works: the article should be rewritten with this in mind.

Comments for author File: Comments.pdf

In my opinion, only minor English corrections are needed

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Reviewer 2 Report

This paper aims to propose an LQR method based on the genetic algorithm, toward overcoming the dilemma of tuning extensive parameters in conventional methods. The detailed design is well described and demonstrated via simulations. However, numerous concerns remain to be addressed.

 

Major concerns

-the literature seems inadequate and old. Please supplement. It may be unfair to claim that “none of the previously mentioned works has considered the optimized feedback control”. Optimized feedback control has been well demonstrated in other fields, such as robotics. Refs. [1-3] may be useful for reference.

- The stability must be analyzed for a controller design paper.

- The contribution is unclear. Although extensive simulations are performed, the explicit comparison is not quantitatively provided. Please clarify.

- How to ensure the fairness of comparing different control methods?

- Discussions are in extreme lack, and the conclusions are therefore superficial.

 

References

[1] “Modeling and Experimental Validation for a Large-Scale and Ultralight Inflatable Robotic Arm,” doi: 10.1109/TMECH.2021.3065046.

[2] “Hybrid adaptive disturbance rejection control for inflatable robotic arms,” doi: 10.1016/j.isatra.2021.08.016.

[3] “A Large-Scale Inflatable Robotic Arm toward Inspecting Sensitive Environments: Design and Performance Evaluation,” IEEE Trans. Ind. Electron, doi: 10.1109/TIE.2022.3232643.

Author Response

Please see the attachment

Author Response File: Author Response.docx

Reviewer 3 Report

The paper is innovative but needs some improvement as follows to be ready for publication in the journal

1-The abstract needs to be revised and compared with previous similar methods better and the superiority of the work should be highlighted. Also significance of research is not mentioned.

2- The history of research needs to be improved and innovation of the research needs to be mentioned better point by point end of the introduction.

3- What is the reason for considering the system dynamics as equation 1? references or proof is needed.

4-There is an assumption for lines 75-78. please clarify why did you consider your research as this assumption? A logical reason, please.

5- What is your justification for hiring the LQR method compared to prior similar methods?

6- Please clarify equation 21.

7- English includes many mistakes. please improve it.

8- How artificial intelligence can improve your work. it is highly recommended to refer to the following works and consider them in the introduction.

Modern Adaptive Fuzzy Control Systems
Neural Networks and Learning Algorithms in MATLAB

9- In"4.3. LQR based on Genetic Algorithm" please improve its novelty of in your text and improve the section.

10- "5. Conclusion" is not well written .

11- What is the limitation of your work?

12- Genetic algorithm has some limitations in many cases compared to other similar methods. why did you apply it? please clarify.

English needs to be improved.

Author Response

Please see the attachment

Round 2

Reviewer 2 Report

The author addressed most of my previous concerns except for only one.  Is there anything sacrificed for using the genetic algorithm technique to design the proposed LQR controller?

Author Response

Thank you for your comment. Since the genetic algorithm in this paper has been constructed mainly to improve the performance in terms of rise time, settling time and maximum overshoot, the control effort required to achieve this target became larger, which may not be compatible with hardware constraints. However, this can be solved if needed by assigning larger weights on the control matrix R of the LQR controller.

Thank you for your constructive feedback

Reviewer 3 Report

Thanks for your revision.

Author Response

Thank you for your constructive feedback.

Author Response File: Author Response.pdf