Experimental and Numerical Study on Trajectory Tracking of Remotely Operated Vehicles Involved in Cleaning Aquaculture Vessels

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

1. Please, provide scientific claim of the paper - what you can do better uesing your control, etc.

2. I didn't find analysis of position measurement, no sensors, no error analysis, no claim on possible tolerances.

3. Conclusion contains no data for resulting improvement, no numerical value presented. Conclusions cannot be derived from results directly, therefore, please, improve it.

Author Response

 

We sincerely thank you for your valuable suggestions amidst your busy schedule. Based on your feedback, we have carefully analyzed and addressed each issue in the paper accordingly.

 

1. Please, provide scientific claim of the paper - what you can do better uesing your control, etc.

 

We greatly appreciate your important suggestion. Based on your feedback, we have comprehensively revised and strengthened the conclusion section to clearly articulate the scientific claim of our study. Specifically, we have

1. Clarified the Scientific Claim  We have explicitly stated the proposed Multi-Scale Dynamic Sliding Mode Adaptive Control (MDSMAC) strategy and detailed its effectiveness in addressing challenges such as structural nonlinearities, parameter uncertainties, external environmental disturbances, and strong operational disturbances in underwater aquaculture cleaning tasks.
2. Highlighted the Advantages of the Method  We emphasized the significant advantages of the MDSMAC strategy over traditional control methods (such as PID Control, Fuzzy Logic Control (FLC), Sliding Mode Control (SMC), and Neural Network Control (NNC)) in terms of trajectory tracking accuracy and system stability. The experimental results demonstrate that MDSMAC exhibits higher accuracy and reliability under complex disturbances and nonlinear systems.
3. Supplemented Experimental Data  To support our scientific claim, we included detailed experimental data in the conclusion, showcasing that MDSMAC achieves rapid trajectory tracking within 0.1 seconds, with mean square errors significantly lower than other intelligent control algorithms, thereby validating its superior performance in dynamic underwater environments.
4. Discussed Research Limitations and Future Directions  We discussed the current limitations of our simulation and experimental setups and proposed future research directions, including testing in more complex and dynamic real-world environments and integrating advanced machine learning techniques to further enhance system performance.

The specific modifications have been detailed in the conclusion section of the revised manuscript.

 

 

2. I didn't find analysis of position measurement, no sensors, no error analysis, no claim on possible tolerances.

 

We greatly appreciate you pointing out the shortcomings in our paper concerning position measurement analysis, sensor information, error analysis, and tolerance statements. Based on your feedback, we have made the following modifications and additions to the manuscript

1. Position Measurement Analysis

In Chapter 4, Section 4.1, we have added the parameters of the Inertial Measurement Unit (IMU) and the pressure-type depth sensor, and provided a detailed explanation of their working principles.

IMU Working Principle  The IMU consists of a three-axis accelerometer and a three-axis gyroscope, which are used to measure the robot's linear acceleration and rotational rates along three orthogonal axes. By fusing data from these sensors, the IMU can provide the robot's orientation and position information, ensuring precise responses from the control system.

Pressure-Type Depth Sensor Principle  This sensor determines the robot's depth by measuring the static water pressure in the underwater environment. As depth increases, water pressure increases linearly, and the sensor converts these pressure changes into corresponding depth values, achieving high-precision and stable depth measurements.

1. Sensor Installation Position and Parameter Description

In Chapter 4, Section 4.1, we have added descriptions of the sensor installation positions and specific parameter.

The IMU's accelerometer range was adjusted to ±2 g with a sensitivity of 16384 LSB/g, and the gyroscope range was set to ±2000 dps with a sensitivity of 16.4 LSB/°/s. The depth sensor can measure depths up to 100 meters with an accuracy of 0.1 meters. The cameras are conventional underwater cameras used for observing the underwater environment and do not possess positioning functionality. All sensors are installed in the grooves of the robot's upper crossbar, ensuring effective data acquisition while preventing physical damage to the sensors during operation.

1. Error Analysis

In Chapter 4, Section 4.4.3, we have added a detailed analysis of position measurement errors, discussing the impact of sensor noise, system latency, and environmental disturbances on the measurement results. This section includes

Sensor Noise: An analysis of the noise generated by the IMU and depth sensors during measurements and its impact on positioning accuracy.

System Latency: A discussion on the delays that may be introduced during data acquisition and processing, and how these delays affect the real-time response of the control system.

Environmental Disturbances: An evaluation of how external factors such as water currents and temperature changes in the underwater environment interfere with sensor measurements.

1. Tolerance Statement

In the Conclusion section, we have added a discussion on the tolerance of the position measurement system.

In this study, we conducted a detailed analysis of the position measurement system's error tolerance. The system is designed to tolerate a maximum measurement error of ±0.1 meters, ensuring that the underwater robot maintains good trajectory tracking performance and system stability even in the presence of measurement inaccuracies. By optimizing sensor placement and refining the control algorithm, the system effectively mitigates external disturbances and internal parameter uncertainties within the allowable error range, thereby enhancing the overall robustness and reliability of the MDSMAC control strategy.

Through the above modifications, we believe that the paper has been significantly improved in terms of position measurement and its related analyses, further enhancing the scientific rigor and practicality of the control strategy.

The specific modifications have been detailed in chapter 4 section 4.1 and 4.4 of the revised manuscript.

 

3. Conclusion contains no data for resulting improvement, no numerical value presented. Conclusions cannot be derived from results directly, therefore, please, improve it.

 

We greatly appreciate you pointing out the lack of data and numerical values in the conclusion section. Based on your feedback, we have made the following modifications and additions to the conclusion to include key experimental data and numerical results, thereby better supporting our conclusions

1. Addition of Numerical Data

In the conclusion section, we have added the mean square errors (0.205, 0.063, and 0.008) and absolute errors (0.269, 0.239, and 0.087) of MDSMAC in the X, Y, and Z directions, respectively. These values effectively quantify the improvement of MDSMAC compared to traditional control methods.

1. Clarification of Improvement Magnitude

We have detailed the specific enhancements of MDSMAC compared to traditional methods in terms of trajectory tracking accuracy and system stability. For example, MDSMAC's mean square errors in each direction are reduced by approximately 70%, 80%, and 90%, respectively, demonstrating its significant advantages under complex disturbances and nonlinear systems.

1. Correlation Between Experimental Results and Conclusions

The conclusion section now integrates the experimental results with the main contributions of the study, ensuring that the conclusions are based on specific data and analyses. For example, through numerical simulations and physical experiments, we have validated the superior performance of MDSMAC under various operating conditions. The specific data show significant reductions in tracking errors across all directions, further proving the effectiveness and robustness of the proposed control strategy.

Through these modifications, the conclusion section now includes specific numerical data and experimental results, providing stronger support for our research conclusions and ensuring logical consistency between the conclusions and the results.

The specific modifications have been detailed in the conclusion section of the revised manuscript.

 

We sincerely appreciate your invaluable suggestions and meticulous guidance throughout the manuscript, which have been instrumental in refining and enhancing our work. We firmly believe that these improvements have not only deepened the academic rigor of our research but have also significantly enhanced the practical implications of our findings. If you have any further comments or suggestions, please feel free to let us know.

Sincerely,

Zhang hua

And xu shuangxi , xie yonghe

 

 

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The paper presents a multi-scale dynamic sliding mode adaptive control strategy for a remotely operated vehicle used in aquaculture vessels, in order to compensate the effects of structural nonlinearities and external disturbances and to achieve a precise trajectory tracking. Starting from a six-degree-of-freedom motion model, an adaptive multi-scale sliding mode control system is designed and developed, while also analyzing its stability using the Lyapunov method. The proposed control strategy is validated both through simulation and water tank experiments, proving the feasibility of the control solution. The article is well written, and the research methodology is well constructed, with clear results for the analyzed case.

 Comments, questions and recommendations for the manuscript improvement:

- I think the first chapter should be titled Introduction, obviously analyzing the challenges in the field of remotely operated vehicles trajectory tracking for aquaculture.

- A comparison with other solutions in the field of remotely operated vehicles control would be useful. Although the references cited in this manuscript are appropriate and relevant to this research, I would suggest adding new bibliographic titles to the 'References' and citing them in the context in which other control solutions are mentioned in the article. Proportional-Integral-Derivative (PID) control, Fuzzy Logic Control (FLC), Sliding Mode Control (SMC), Model Predictive Control (MPC), and Neural Network Control (NNC). Is the proposed solution better?

- Each relationship/equation must be explicitly referred to by its number within the textual body of the article. So, instead of "as follow", the equation/relationship number should be explicitly stated.

-There are many methods to prove the stability of a control system. Why did you choose the second Lyapunov method?

- The Conclusions chapter should be extended to clearly highlight the main contribution of the research. What advantages does the proposed solution have? Obviously, a particular case is analyzed here. Can the conclusions be generalized?

Author Response

Thank you for your valuable suggestions amidst your busy schedule. Based on your feedback, we have carefully analyzed and addressed each issue in the paper accordingly.

 

- I think the first chapter should be titled Introduction, obviously analyzing the challenges in the field of remotely operated vehicles trajectory tracking for aquaculture.

Thank you for your valuable feedback. I have renamed Chapter 1 as "Introduction." In this section, the paper has been expanded to include background information on the control of underwater remotely operated vehicles (ROVs) in aquaculture, and a detailed analysis of the challenges faced by the aquaculture industry. Specifically, the impact of biofouling on water quality and equipment operation has been discussed, highlighting the significant effect of this issue on the sustainability and economic viability of aquaculture. Additionally, the application of ROVs in aquaculture has been introduced, and the challenges encountered during trajectory tracking have been examined, providing solid background support for the subsequent research.

The specific modifications have been detailed in the Introduction of the revised manuscript.

Once again, thank you for your insightful suggestions!

 

- A comparison with other solutions in the field of remotely operated vehicles control would be useful. Although the references cited in this manuscript are appropriate and relevant to this research, I would suggest adding new bibliographic titles to the 'References' and citing them in the context in which other control solutions are mentioned in the article. Proportional-Integral-Derivative (PID) control, Fuzzy Logic Control (FLC), Sliding Mode Control (SMC), Model Predictive Control (MPC), and Neural Network Control (NNC). Is the proposed solution better?

 

Thank you for your valuable suggestions! Based on your feedback, we have made improvements and enhancements to the control solutions in the manuscript, as detailed below:

1. Introduction Section:
   We have added a review of existing solutions in the field of remotely operated vehicles (ROVs) control and introduced new references to ensure the breadth and timeliness of the literature. The discussion on methods such as PID control, Fuzzy Logic Control (FLC), Sliding Mode Control (SMC), Model Predictive Control (MPC), and Neural Network Control (NNC) has been expanded, clearly highlighting their advantages and limitations. For example, although PID is widely used, it has limitations in dynamic systems and under strong external disturbances. While MPC can handle complex disturbances, its computational complexity limits its real-time application.
2. Algorithm Section:
   We have conducted simulation comparisons between the proposed control strategy and traditional methods (e.g., PID and FLC), ensuring all comparisons were made under the same conditions. The proposed MDSMAC strategy combines the advantages of fuzzy logic and sliding mode control, significantly improving the system’s adaptability and robustness. Simulation results show that MDSMAC demonstrates stronger trajectory tracking accuracy and higher system stability under multi-scale disturbances and dynamic environments.
3. Comparison with Existing Methods:
   By comparing with PID, FLC, SMC, MPC, and other methods, we validated the superiority of our approach in complex environments, highlighting the limitations of existing methods and showcasing the innovation and advantages of our proposed solution.

   The specific modifications have been detailed in the Introduction, Algorithm Simulation, and Conclusion sections of the revised manuscript.

 

- Each relationship/equation must be explicitly referred to by its number within the textual body of the article. So, instead of "as follow", the equation/relationship number should be explicitly stated.

 

Thank you for your valuable suggestions! Based on your feedback, We have revised the manuscript as per your suggestion. All relationships and equations mentioned in the text have now been explicitly numbered. Each relevant equation is clearly identified by its number, eliminating vague expressions such as "as follows." This revision ensures that the derivations and discussions in the manuscript are more precise and easier to reference.

The specific modifications have been detailed in Chapters 2 and 3, specifically in the formula sections of the revised manuscript.

 

-There are many methods to prove the stability of a control system. Why did you choose the second Lyapunov method?

Thank you for your valuable feedback.

In this study, we chose the second Lyapunov method to analyze the stability of the proposed control strategy. The Lyapunov method is particularly suitable for the stability analysis of nonlinear systems and exhibits strong robustness to disturbances. For complex systems such as underwater robots, the Lyapunov method ensures global stability by constructing an appropriate Lyapunov function, without relying on system linearization. One significant advantage of this method is its ability to effectively address stability issues in nonlinear systems under strong disturbances, which is of great importance for practical applications.

In comparison to the Linear Matrix Inequality (LMI) method, while LMI performs well with linear or quasi-linear systems, its adaptability to strongly nonlinear systems is relatively poor, and it requires more precise system modeling. In some cases, the LMI method may not be as flexible as the Lyapunov method, especially when dealing with complex underwater environments and external disturbances.

Moreover, other methods, such as the small gain theorem and transfer function methods, are also commonly used for stability analysis. However, these methods often require strong system linearization assumptions. In contrast, the Lyapunov method provides a more comprehensive and applicable analysis framework for nonlinear systems, such as underwater robots. Using the second Lyapunov method, we not only guarantee system stability but also achieve better robustness in the ever-changing underwater environment.

Therefore, the choice of the second Lyapunov method for stability analysis in this study not only ensures the system's stability but also effectively handles the system's nonlinear characteristics and external disturbances, providing strong theoretical support.

This content has been added to the manuscript.

   In response to this issue, we have provided a detailed explanation at the beginning of the Theoretical Proof section in Chapter 2, Section 2.5.

- The Conclusions chapter should be extended to clearly highlight the main contribution of the research. What advantages does the proposed solution have? Obviously, a particular case is analyzed here. Can the conclusions be generalized?

 

We appreciate your insightful suggestion to extend the conclusions chapter and clearly highlight the main contributions of our research. Based on your feedback, we have revised the conclusion as follows:

1. Clarification of Contributions and Advantages:
   We fully agree that the conclusion should better emphasize the main contributions of the study and the advantages of the proposed solution. In the revised conclusion, we have specifically enhanced the description of the innovative aspects and advantages of the MDSMAC strategy. We emphasize the unique advantages of MDSMAC over traditional control methods (e.g., PID control, sliding mode control) in handling dynamic disturbances and system uncertainties in underwater environments, particularly in terms of trajectory tracking accuracy and system stability over long-duration tasks. Through numerical simulations and experimental validation, we further highlight the effectiveness of MDSMAC in underwater robot control, particularly its performance in complex environments.
2. Generalizability:
   Your suggestion regarding the generalizability of the conclusions is well taken. While the study focuses on underwater robots, we believe the MDSMAC strategy has broad application potential, especially in dynamic and complex environments. In the conclusion, we have explicitly stated that MDSMAC is not limited to underwater robots, but could also be applied to autonomous robotic systems, automated cleaning, and specialized industrial applications. We highlight the robustness of the control strategy in complex environments and discuss its potential in other industrial and automation fields, particularly in scenarios demanding high-precision control, such as intelligent manufacturing and industrial automation.
3. Future Research Directions:
   Regarding future research, we strongly agree with your suggestion to incorporate machine learning to enhance the system's adaptability. Therefore, we have added a discussion on future research, particularly focusing on the integration of deep learning and reinforcement learning techniques for adaptive parameter tuning. This would further improve the system's precision, robustness, and adaptability. We believe that incorporating machine learning techniques can enable MDSMAC to achieve better self-learning and optimization capabilities in dynamic, complex environments, thus expanding its applicability to more diverse real-world scenarios.

This issue has been revised and discussed in the conclusion section.

 

We sincerely appreciate your invaluable suggestions and meticulous guidance throughout the entire manuscript, which have been instrumental in refining and enhancing various aspects of our work. We firmly believe that these improvements have not only deepened the academic rigor of the research but have also significantly strengthened the practical potential of the findings. If you have any further comments or suggestions, please feel free to let us know.

Sincerely,

Zhang hua

And xu shuangxi , xie yonghe

Author Response File: Author Response.pdf