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Article
Peer-Review Record

Adaptive Robust Path Tracking Control for Autonomous Vehicles Considering Multi-Dimensional System Uncertainty

World Electr. Veh. J. 2023, 14(1), 11; https://doi.org/10.3390/wevj14010011
by Mengyuan Chen 1, Yue Ren 2,* and Minghui Ou 1
Reviewer 1: Anonymous
Reviewer 2:
Reviewer 3: Anonymous
World Electr. Veh. J. 2023, 14(1), 11; https://doi.org/10.3390/wevj14010011
Submission received: 28 November 2022 / Revised: 20 December 2022 / Accepted: 27 December 2022 / Published: 2 January 2023

Round 1

Reviewer 1 Report

This article is relatively solid in its work but lacks some details describing the neural network used in the article.

 

I recommend adding a brief introduction to the RBFNN and NFTSM training data sets to make this article more complete.

Author Response

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Author Response File: Author Response.docx

Reviewer 2 Report

An adaptive robust path tracking control system has been proposed. the paper compared two simulation cases: with and without MARC. However the one without MARC for comparison was not claimed to represent any state-of-the-art model in this category. 

the simulation case is set as a 2 meters maneuver, however the missing key data for longitudinal position in Fig 4(a). No detail is described why the simulation case is set the way it is in the paper.

Overall the paper doesn't show enough evidence for the proposed control system exceed the performance of the current state-of-the-art. 

detail questions:

- first row of formula (2), why the angle is the heading angle, not the steering while angle.

- formula (3), G is not defined.

- formula (32) what is Ts and Vs(0)?

- line 266 , equation (42) or (41)?

- line 276. Table 1. I am not sure what information this table try to present

- line 298, lager or Larger?

- line 308, what is "wording conditions"?

- line 318 "with out"

Author Response

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Author Response File: Author Response.docx

Reviewer 3 Report

-The paper proposes a control architecture for vehicle lateral control. The controller estimates the system's uncertainty with a Radial Basis Function Neural Network.

- In the introduction, the contribution is defined as ANFSTM (Adaptive Non Singular Fast Terminal Sliding Mode), however, this acronym does not appear at all in the document. Instead, NFTSM without "A" is used throughout the document.

- Both redaction and grammar of all sections must be improved. The paper is hard to read. 

- Acronyms appear before being defined.

- Table 1 on line 276 is empty.

- Results are not compared to other state-of-the-art control techniques, or at least the comparison is not clearly stated.

Author Response

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Author Response File: Author Response.docx

Round 2

Reviewer 2 Report

Thanks for the clarification. Nice work!

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