Real-Time Ocean Current Compensation for AUV Trajectory Tracking Control Using a Meta-Learning and Self-Adaptation Hybrid Approach
Abstract
:1. Introduction
- We employ deep learning theory to improve the adaptive control method instead of completely relying on DNN output. To the best of our knowledge, this is the first combination of meta-learning and adaption in AUV control.
- We evaluate the effectiveness of the proposed method by implementing it in the simulation of a fully actuated six-DOF AUV.
- We provide theoretical guarantees for the feasibility, stability, generalization, and low time complexity of the MAC, which ensures the practical significance of our approach.
2. Modeling and Problem Formulation
2.1. Assumptions
- The AUV is fairly symmetrical about its three planes.
- The center of buoyancy of the AUV is located on the geometric symmetry plane.
- The AUV is considered a rigid body; thus, there are no bending or geometrical deformations.
- The entire AUV body is completely submerged in water.
2.2. Modeling
2.3. DNN Input and Output
2.4. Meta-Learning Goal
2.5. Adaptive Control Goal
3. Meta-Learning-Based Adaptive Control
3.1. Assumptions
- The direction of currents comes from the positive y-axis in the c-frame, but a small range of disturbance is added to the three axes in the c-frame.
- Disturbance is consistent with additive white Gaussian noise (AWGN) with a variance of 0.01.
3.2. Data Collection
3.3. Preparation for Meta-Learning
3.4. Meta-Learning
Algorithm 1 Meta-Learning Algorithm |
|
3.5. Adaptive Control
4. Simulation Results and Discussion
4.1. DNN Training
4.2. Simulate the Effects of Ocean Currents on AUV
4.3. Control Performance
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Hyperparameter | Value |
---|---|
Architecture of net | |
Architecture of h net | |
k | 64 |
256 | |
0.1 | |
l | 0.001 |
0.0005 | |
15 | |
q | 0.5 |
2 | |
500 |
Methos | RMSE | Unit | |||
---|---|---|---|---|---|
PID1 | 1.157 | 1.629 | 0.016 | 1.998 | m |
PID2 | 1.679 | 1.961 | 0.017 | 2.582 | m |
PID3 | 1.893 | 2.984 | 0.021 | 3.533 | m |
PID4 | 2.381 | 6.161 | 0.184 | 6.605 | m |
PID5 | 12.175 | 15.844 | 0.045 | 19.982 | m |
Average | 3.857 | 5.716 | 0.057 | 6.94 | m |
Methos | RMSE | Unit | |||
---|---|---|---|---|---|
Baseline | 1.157 | 1.629 | 0.016 | 1.998 | m |
MAC | 0.596 | 1.235 | 0.058 | 1.373 | m |
Adaptive | 3.599 | 3.452 | 0.048 | 4.987 | m |
Meta-Learning | 15.732 | 11.209 | 0.062 | 19.317 | m |
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Zhang, Y.; Che, J.; Hu, Y.; Cui, J.; Cui, J. Real-Time Ocean Current Compensation for AUV Trajectory Tracking Control Using a Meta-Learning and Self-Adaptation Hybrid Approach. Sensors 2023, 23, 6417. https://doi.org/10.3390/s23146417
Zhang Y, Che J, Hu Y, Cui J, Cui J. Real-Time Ocean Current Compensation for AUV Trajectory Tracking Control Using a Meta-Learning and Self-Adaptation Hybrid Approach. Sensors. 2023; 23(14):6417. https://doi.org/10.3390/s23146417
Chicago/Turabian StyleZhang, Yiqiang, Jiaxing Che, Yijun Hu, Jiankuo Cui, and Junhong Cui. 2023. "Real-Time Ocean Current Compensation for AUV Trajectory Tracking Control Using a Meta-Learning and Self-Adaptation Hybrid Approach" Sensors 23, no. 14: 6417. https://doi.org/10.3390/s23146417
APA StyleZhang, Y., Che, J., Hu, Y., Cui, J., & Cui, J. (2023). Real-Time Ocean Current Compensation for AUV Trajectory Tracking Control Using a Meta-Learning and Self-Adaptation Hybrid Approach. Sensors, 23(14), 6417. https://doi.org/10.3390/s23146417