Research on Trajectory Tracking Control of Driverless Electric Formula Racing Car Based on Game Theory

Round 1

Reviewer 1 Report

It is of great practical significance to realize the dual-objective optimization of trajectory tracking accuracy and driving stability by improving MPC algorithm. By determining the evolutionary game subject, establishing the game gain matrix, determining the weights of each subject, and constructing a dynamic replication system for weight evolution to find the optimal equilibrium strategy for model prediction controller pair was improved to optimize the shortcomings of the traditional algorithm and improve the efficiency of the algorithm. And based on the evolutionary game theory, the path tracking controller is designed to verify the feasibility of the planned path. The results have important value,  but The following revisions should also be made.

1: The academic terminology should be unified, and it is suggested that all game theory-based MPC controllers in the paper should be changed to game theory based MPC controller. Please carefully check other issues.

2: This article lacks an introduction to the evolutionary game. The differences between evolutionary games and other game models, and the advantages of evolutionary games are not explained. The authors could have described this more.

3:Try to be consistent with the text and images. For example, in the paper “4. Trajectory tracking control simulation verification” in the subheadings are suggested to be consistent with those in the control strategy of Fig. 1 “Verifying the Stability of the Evolutionary Game”.

Author Response

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Reviewer 2 Report

This paper presents a game-theoretic-based trajectory tracking control method for the dual-objective optimization problem of trajectory tracking accuracy and driving stability of driverless electric formula racing cars. The simulation results show that the designed controller has better performance in trajectory tracking accuracy and driving stability compared to a single model predictive controller. There are still some concerns for me.

1.        In the simulation verification section, how does the steering angle of front wheels change with time?

2.        Section 4 mentions that "the MPC controller selects the weight matrix coefficients with better simulation results", please explain this selection process in detail.

3.        In the conclusion, it is mentioned that “The model prediction controller based on the evolutionary game of both sides has good trajectory tracking with strong robustness at high, medium, and low speeds.” Please explain "strong robustness" according to this paper.

4.        This description “Figure 6(f) shows that the maximum center of mass slip angle of the MPC controller is 150 deg, and the maximum center of mass slip angle of the game-theoretic-based MPC controller is 2 deg, which is 98.6% better than the MPC controller.” doesn’t correspond to Figure 6(f).

5.        There is no title for Figure 1.

6.        The title of section 3.1 is the same as the title of section 3.2.

Author Response

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Reviewer 3 Report

Observations in the file atached.

Comments for author File: Comments.pdf

Author Response

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Round 2

Reviewer 2 Report

The authors have addressed my concerns. It can be accepted as is.

Author Response

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Reviewer 3 Report

Observations in the attached file

Comments for author File: Comments.pdf

Author Response

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Author Response File: Author Response.docx