Optimizing Unmanned Air–Ground Vehicle Maneuvers Using Nonlinear Model Predictive Control and Moving Horizon Estimation

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

1) The paper presents a method for the distributed control of a heterogeneous fleet comprising a steering vehicle and quadrocopter, using a combination of nonlinear model predictive control (NMPC) and nonlinear moving horizon estimation (NMHE).

2) The NMHE is used to estimate unmeasured quantities for both robots, and to reconcile measurements. The NMPC is used for control generation and trajectory prediction.

3) The relevance of the paper lies in its consideration of effective approaches to constructing cooperative control between heterogeneous robotic systems, making a significant contribution to the advancement of control of complex systems.

4) The efficacy of the method has been quantitatively and qualitatively verified through the simulation results provided.

5) The paper lacks a concluding section that summarizes the findings of the study. The bibliography list is correctly formatted.

6) All mathematical expressions are presented correctly. Figures and tables accurately depict the results.

Author Response

Please see the attachment

 

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The paper presents an algorithm that combines Nonlinear Model Predictive Control (NMPC) and Nonlinear Moving Horizon Estimator (NMHE) to control in a distributed way a heterogeneous fleet composed of a steering car and a quadcopter. Although the obtained results are interesting, I regret to inform you that the paper cannot be published in its current version. Here are several suggestions for improvement:

1. The proposed method appears to simply combine two existing approaches without demonstrating a necessary or logical connection between them, which undermines the overall novelty and originality of the paper.

2. There is an excessive amount of text dedicated to modeling the UAV and UGV, which seems redundant as this aspect has already been comprehensively addressed by previous research.

3. The paper does not provide any proof of stability or convergence for the proposed method, leaving a significant gap in the validation of its reliability and robustness.

4. The simulations presented in the paper are insufficient to convincingly demonstrate the effectiveness of the proposed method, indicating a need for more comprehensive testing and validation.

5. The references cited in the paper are relatively outdated. To strengthen the scientific foundation of the work, it is necessary to include more recent publications within the past three years. This will demonstrate the authors' familiarity with the latest advancements in the field and provide readers with up-to-date references for further exploration.

 

6. The paper contains grammatical errors and issues with English tense usage. The authors should diligently address these language-related problems to improve the overall clarity and coherence of the manuscript.

Comments on the Quality of English Language

The paper contains grammatical errors and issues with English tense usage. The authors should diligently address these language-related problems to improve the overall clarity and coherence of the manuscript.

Author Response

Please see the attachment

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

See the comments in the uploaded file.

Comments for author File: Comments.pdf

Comments on the Quality of English Language

Minor editing of English language required.

Author Response

Please see the attachment

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

The reply is satisfactory. No more comments.

Reviewer 3 Report

Comments and Suggestions for Authors

The comments have been handled.

Comments on the Quality of English Language

Moderate editing of English language required.