Artificial Intelligence-Enhanced UUV Actuator Control
Abstract
:1. Introduction
1.1. Motivation
1.2. Literature Review
The proposed method is based on feedforward self-awareness using process dynamics.
Feedforward self-awareness using process dynamics is foundational for the proposed method.
1.3. Research Gap and Authors Contribution
- Main Conclusion of the study
- Recommendation of key threshold discretization and computational speed to duplicate the results of the original prequel [3].
- Validation of the first sequel’s [14] identification of paramountcy of discretization and computational speed.
- Validation of the second sequel’s [13] identification of deterministic artificial intelligence performance and recommended selection of algorithms.
1.4. Organization
2. Materials and Methods
2.1. Truth Model for Motor Dynamics
2.2. Model-Following Self Tuner
2.3. Deterministic Artificial Intelligence
3. Results
3.1. Model-Following Self-Tuner Control vs. Deterministic Artificial Intelligence
3.2. Discrete Deterministic Artificial Intelligence vs. Continuous Deterministic Artificial Intelligence
4. Discussion
4.1. Concluding Remarks
4.2. Recommended Future Research
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Appendix A.1. Discrete Deterministic Artificial Intelligence
Appendix A.2. Continuous Deterministic Artificial Intelligence
Appendix A.3. All Deterministic Artificial Intelligence
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Variable/Acronym | Definition | Variable/Acronym | Definition |
---|---|---|---|
Transfer function | Differential variable | ||
Output | Difference variable | ||
Input | Discrete time variable |
Variable/Acronym | Definition | Variable/Acronym | Definition |
---|---|---|---|
Control input | Regressor matrix | ||
Desired output | Parameter vector | ||
Estimates | True values | ||
Proportional gain | Difference gain | ||
Coefficients of | Coefficients of |
Method | Step Size (s) | Error Mean | Error Standard Deviation |
---|---|---|---|
Deterministic artificial intelligence | 0.60 | 6.3918 | 4.8100 |
Model-following | 0.60 | 0.0264 | 0.0973 |
Deterministic artificial intelligence | 0.50 | 0.0956 | 0.1632 |
Model-following | 0.50 | 0.0277 | 0.0917 |
Deterministic artificial intelligence | 0.27 | 0.0175 | 0.0545 |
Model-following | 0.27 | 0.0471 | 0.1745 |
Deterministic artificial intelligence | 0.20 | 0.0114 | 0.0487 |
Model-following | 0.20 | 0.0608 | 0.2446 |
Deterministic Artificial Intelligence Type | Step Size (s) | Error Mean | Error Standard Deviation |
---|---|---|---|
Discrete | 0.50 | 0.0956 | 0.1632 |
Continuous | 0.50 | 0.0223 | 0.1654 |
Discrete | 0.20 | 0.0114 | 0.0487 |
Continuous | 0.20 | 0.0122 | 0.1401 |
Method | Step Size (s) | Error Mean | Error Standard Deviation |
---|---|---|---|
DAI | 0.60 | 6586% | 2847% |
MF | 0.60 | −72% | −40% |
DAI | 0.50 | 0% | 0% |
MF | 0.50 | −71% | −44% |
DAI | 0.27 | −82% | −67% |
MF | 0.27 | −51% | 7% |
DAI | 0.20 | −88% | −70% |
MF | 0.20 | −36% | 50% |
Deterministic Artificial Intelligence Type | Step Size (s) | Error Mean | Error Standard Deviation |
---|---|---|---|
Discrete | 0.50 | 0% | 0% |
Continuous | 0.50 | −77% | 1% |
Discrete | 0.20 | −88% | −70% |
Continuous | 0.20 | −87% | −14% |
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Wang, Z.; Sands, T. Artificial Intelligence-Enhanced UUV Actuator Control. AI 2023, 4, 270-288. https://doi.org/10.3390/ai4010012
Wang Z, Sands T. Artificial Intelligence-Enhanced UUV Actuator Control. AI. 2023; 4(1):270-288. https://doi.org/10.3390/ai4010012
Chicago/Turabian StyleWang, Zhiyu, and Timothy Sands. 2023. "Artificial Intelligence-Enhanced UUV Actuator Control" AI 4, no. 1: 270-288. https://doi.org/10.3390/ai4010012