| 李亚龙,王俊雄.考虑未知时变流速的AUV改进动态面自适应跟踪控制[J].装备环境工程,2025,22(1):144-151. LI Yalong,WANG Junxiong.Improved Dynamic Surface Adaptive Tracking Control of AUV Considering Unknown Time-varying Velocity[J].Equipment Environmental Engineering,2025,22(1):144-151. |
| 考虑未知时变流速的AUV改进动态面自适应跟踪控制 |
| Improved Dynamic Surface Adaptive Tracking Control of AUV Considering Unknown Time-varying Velocity |
| 投稿时间：2024-09-03 修订日期：2024-09-30 |
| DOI：10.7643/issn.1672-9242.2025.01.015 |
| 中文关键词: 水下机器人 动态面控制 未知时变海流速度 自适应控制 轨迹跟踪 径向基神经网络中图分类号:U674.941 TP242 文献标志码:A 文章编号:1672-9242(2025)01-0144-08 |
| 英文关键词:autonomous underwater vehicles dynamic surface control unknown time-varying ocean current velocity adaptive control trajectory tracking radial basis function neural network |
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| 中文摘要: |
| 目的 提高水下机器人在未知时变海流速度、不确定性建模和环境干扰3种未知因素影响下的跟踪控制性能。方法 基于改进动态面自适应控制方法，首先为补偿三种未知因素的影响，设计海流速度自适应更新律和径向基神经网络，对其进行实时估计，同时将传统的固定滤波器改进为一种时变滤波器，以改善控制输入抖振问题。然后构建Lyapunov函数证明稳定性。最后进行仿真实验，并与传统动态面控制法和反步滑模控制法作对比。结果 本文设计的海流速度自适应更新律和径向基神经网络能够精确估计3种未知因素的影响，展现了强大的鲁棒性。此外，相比于2种对比方法，本文方法在控制精度、解决抖振能力方面展现了优越的控制性能。结论 基于改进动态面自适应控制方法，在考虑不确定性建模和环境干扰的基础上，解决了现实情况中存在的未知时变海流速度干扰问题，同时提高了水下机器人在复杂环境中的控制性能。 |
| 英文摘要: |
| 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. |
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