Pulse Train Fx-LMS Algorithm for Drive File Identification

Round 1

Reviewer 1 Report

The paper by Balasubramanya and Southward introduces a novel algorithm for the identification of drive files for service environment replication applications. The paper is clearly written and the methodology is sound and supported by the tests on three different case studies, conducted on experimental data from an actual vehicle suspension.

The proposed algorithm is compared with an existing commercial DFID algorithm, showing comparable performances and a more rapid convergence, which requires a lower number of batches.

I think the approach illustrated in the paper may be of interest to the scientific community and therefore I recommend it for publication. 

Author Response

Thank you very much for taking the time to review the manuscript. We really appreciate your positive feedback on the preparation of the manuscript for publication. 

Reviewer 2 Report

This paper presents a novel time-domain algorithm, the Pulse Train Filtered-X Least Mean Square (PT-Fx-LMS), for the iterative estimation of drive files, aiming to generate dynamic time-series commands for Service Environment Replication (SER) test rigs. The paper demonstrates the rapid convergence and targeted iteration capability of the algorithm on a nonlinear single-degree-of-freedom suspension system through simulation studies. Here are some suggestions for improving the paper, aiming to enhance the depth and breadth of the research:

(1) The paper should discuss in more detail the applicability and effectiveness of the PT-Fx-LMS algorithm for different types of dynamic systems.

(2) Introduce more performance evaluation metrics, such as the number of iterations and the trend of the adaptation function, to comprehensively assess the performance of the algorithm.

(3) Design targeted tests, such as adding noise to the input signal or intentionally introducing model errors, to evaluate the robustness of the algorithm.

(4) Outline the direction for future work, including how to further improve the performance of the algorithm, reduce computational resource consumption, and enhance the practicality of the algorithm.

(5) Cite the latest research findings to ensure the timeliness and cutting-edge nature of the paper content.

English language is fine. 

Author Response

Please see the attachment. 

Author Response File: Author Response.pdf