级联指导,状态估计和控制用于深度跟踪未经调节的自动水下车辆
Yang Qu1, Qin Zhang2, Xianbo Xiang3
1School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, Wuhan, China.
Communications engineering
|November 14, 2025
概括
我们为自主水下车辆开发了一个新的控制系统,通过订购子系统融合,确保稳定的深度跟踪. 这种时间序列融合系统显著减少了振荡,并提高了性能,即使在干扰下.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制系统工程 控制系统工程
- 海洋工程 海洋工程
背景情况:
- 级联指导,状态估计和控制系统对于自动水下车辆 (AUV) 来说至关重要.
- 这些子系统中的无序收序列可能导致振荡和不稳定.
- 现有的系统经常在精确的深度跟踪方面扎,特别是在动态条件下.
研究的目的:
- 引入一种新的时间序列融合级联指导,状态估计和控制系统.
- 为了提高深度跟踪的稳定性和效率,用于未经调节的AUV.
- 解决造成系统不稳定的分系统趋同问题.
主要方法:
- 实现了一个有序融合架构,具有特定的规划时间窗口:状态估计 (td),控制执行 (tc) 和指导 (tf).
- 确保遵守时间序列-融合标准:td < tc < tf.
- 通过对基线和替代方法进行实验性比较来验证系统.
主要成果:
- 拟议的时间测序-融合系统表现出稳定和高效的深度跟踪性能.
- 违反趋同标准的实验显示出显著的振荡反应.
- 在外部干扰下实现了1.32厘米的降低平均深度跟踪误差.
- 展示了有效的攻击角度补偿,尽管有投球错误,但仍然保持了深度跟踪精度.
结论:
- 时间序列融合架构通过确保有序的子系统融合,有效地稳定了AUV深度跟踪.
- 与现有方法相比,这种方法提供了更高的性能,特别是在处理外部干扰时.
- 该系统的稳定性和补偿能力突出显示了其在先进的AUV应用中的潜力.
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