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| A Robust Control Method for AUV Based on High Order Recurrent Neural Networks |
| Received:October 30, 2023 Revised:December 06, 2023 |
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| DOI：10.7643/issn.1672-9242.2024.02.011 |
| KeyWord:autonomous underwater vehicles trajectory tracking high order recurrent neural network HJI theory robust control Lyapunov stability analysis |
| Author | Institution |
| LI Zhengyuan |
State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai , China |
| WANG Junxiong |
State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai , China |
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| Abstract: |
| The modeling uncertainties and external unknown disturbances, among other factors, impose higher demands on the control methods for Autonomous Underwater Vehicle (AUV) in terms of trajectory tracking. The work aims to propose an AUV robust control method based on high-order recurrent neural networks to address it. High-order recurrent neural networks with simple structure but superior approximation performance were employed to estimate modeling uncertainties and external unknown disturbances, which were then compensated for in the input control law to enhance control performance. Subsequently, the neural network weight adaptive update law and AUV adaptive control law were derived based on the HJI theory and Lyapunov stability analysis. Finally, a backstepping sliding mode method was designed as a comparative approach, and simulation experiments were conducted. The experimental results indicated that the proposed AUV robust control method based on high-order recurrent neural networks outperformed the backstepping sliding mode method in terms of tracking error, settling time, and other control metrics. Simulation experiments demonstrate that the proposed robust control method can effectively facilitate precise target trajectory tracking by AUVs, while simultaneously exhibiting excellent control performance and robustness. This research provides an efficient and effective approach for AUV trajectory tracking control, with the potential for application in complex and uncertain underwater environments. |
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