This article presents an innovative approach to optimize steering control for remotely operated underwater vehicles (ROVs) using a genetic algorithm-based proportional integral controller (GAPI). The gain parameters of the PI controller are tuned using a genetic algorithm (GA), and the performance of GAPI is compared with that of a root-locus-tuned PI controller and a simulated-annealing-based PI controller (SAPI). The experimental response corresponding to the step waveform for the GA is obtained, and various response characteristics such as overshoot, peak time, and settling time are benchmarked. The findings indicate that GAPI provides considerably better performance compared to SAPI and the root-locus-tuned controller. This study highlights the effectiveness of using meta-heuristic optimization techniques like genetic algorithms in optimizing relatively straightforward controllers like PI controllers. It suggests that similar optimization techniques can be applied to develop more advanced controllers for sophisticated systems in future research. The authors' contributions include conceptualization, methodology development, software implementation, validation, formal analysis, investigation, data curation, original draft preparation, visualization, supervision, and project administration. All authors have read and agreed to the published version of the manuscript. It is worth noting that this research received no external funding or Institutional Review Board approval since it did not involve human subjects. In conclusion, this article demonstrates how GAPI outperforms other tuning methods like simulated annealing and root-locus tuning in terms of steering control for ROVs. This study opens up possibilities for further research on applying similar optimization techniques to develop advanced controllers for complex systems.
- Innovative approach: Genetic algorithm-based proportional integral controller (GAPI) for optimizing steering control of ROVs
- Comparison with other tuning methods: GAPI vs root-locus-tuned PI controller and simulated-annealing-based PI controller (SAPI)
- Performance evaluation: GAPI provides considerably better performance in terms of overshoot, peak time, and settling time
- Effectiveness of meta-heuristic optimization techniques: Genetic algorithms can optimize relatively straightforward controllers like PI controllers
- Potential for future research: Similar optimization techniques can be applied to develop advanced controllers for complex systems
- Authors' contributions: Conceptualization, methodology development, software implementation, validation, analysis, investigation, data curation, draft preparation, visualization, supervision, project administration
- Funding and approval information: No external funding or Institutional Review Board approval was involved in the research.
Researchers have created a new way to control underwater robots using a special computer program called GAPI. They compared this new method with two other ways of controlling the robots. The GAPI method performed better in terms of how fast and accurately the robots moved. The researchers also found that genetic algorithms, which are a type of computer program, can be used to make simple controllers work even better. In the future, similar methods could be used to create advanced controllers for more complicated systems. The authors of the study did everything from coming up with the idea to analyzing the results and writing about them. The research was not funded by anyone outside of the research team and did not require any special approvals."
Definitions
- Genetic algorithm: A computer program that helps find the best solution to a problem by imitating natural selection.
- Proportional integral controller (PI controller): A type of computer program that helps control how something moves or works.
- Optimizing: Making something work as well as possible.
- Overshoot: When something goes past where it is supposed to go.
- Peak time: How long it takes for something to reach its highest point.
- Settling time: How long it takes for something to stop moving and stay still.
- Meta-heuristic optimization techniques: Special ways of making things work better by trying different options and learning from them.
- Institutional Review Board approval: Permission from a group of experts who make sure that research is done ethically and safely.
Optimizing Steering Control for Remotely Operated Underwater Vehicles (ROVs) Using a Genetic Algorithm-Based Proportional Integral Controller (GAPI)
Remotely operated underwater vehicles (ROVs) are used in various applications such as exploration, surveillance, and research. To ensure their efficient operation, it is important to optimize the steering control of ROVs. This paper presents an innovative approach to optimize steering control for ROVs using a genetic algorithm-based proportional integral controller (GAPI). The gain parameters of the PI controller are tuned using a genetic algorithm (GA), and the performance of GAPI is compared with that of a root-locus-tuned PI controller and a simulated-annealing-based PI controller (SAPI).
Methodology
The GA was used to tune the gain parameters of the PI controller. The experimental response corresponding to the step waveform for the GA was obtained, and various response characteristics such as overshoot, peak time, and settling time were benchmarked. The performance of GAPI was then compared with that of SAPI and root-locus tuning methods.
Results
The findings indicate that GAPI provides considerably better performance compared to SAPI and root-locus tuning methods in terms of steering control for ROVs.
Conclusion
This study highlights the effectiveness of using meta-heuristic optimization techniques like genetic algorithms in optimizing relatively straightforward controllers like PI controllers. It suggests that similar optimization techniques can be applied to develop more advanced controllers for sophisticated systems in future research. This study opens up possibilities for further research on applying similar optimization techniques to develop advanced controllers for complex systems.
