|
| Improved Dynamic Surface Adaptive Tracking Control of AUV Considering Unknown Time-varying Velocity |
| Received:September 03, 2024 Revised:September 30, 2024 |
| View Full Text View/Add Comment Download reader |
| DOI：10.7643/issn.1672-9242.2025.01.015 |
| KeyWord:autonomous underwater vehicles dynamic surface control unknown time-varying ocean current velocity adaptive control trajectory tracking radial basis function neural network |
| Author | Institution |
| LI Yalong |
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 |
|
| Hits: |
| Download times: |
| Abstract: |
| The work aims to enhance the tracking control performance of autonomous underwater vehicles under the effects of three unknown factors of unknown time-varying ocean current velocity, uncertainty modeling, and environmental disturbances. Based on the improved dynamic surface adaptive control method, to compensate for the effects of these three unknown factors, an adaptive updating law for ocean current velocity and a radial basis function neural network were designed for real-time estimation. At the same time, the traditional fixed filter was modified into a time-varying filter to mitigate control input chattering. Subsequently, a Lyapunov function was constructed to prove the stability of the system. Simulation experiments were conducted and compared with traditional dynamic surface control and back stepping sliding mode control methods. The designed adaptive updating law for ocean current velocity and the radial basis function neural network accurately estimated the effects of the three unknown factors, demonstrating robust performance. Furthermore, compared to the other two methods, the proposed method exhibited superior control performance in terms of control accuracy and the ability to resolve chattering. In conclusion, the improved dynamic surface adaptive control method effectively addresses the issue of unknown time-varying ocean current velocity in real-world scenarios, taking into account uncertainty modeling and environmental disturbances, while also improves the control performance of autonomous underwater vehicles in complex environments. |
| Close |
|
|
|