Zhang Yanxin, Li Shaoyuan
Key Laboratory of System Control and Information Processing, Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China.
Sensors (Basel). 2024 Dec 21;24(24):8182. doi: 10.3390/s24248182.
The suspended sediment plume generated in the deep-sea mining process significantly impacts the marine environment and seabed ecosystem. Accurate boundary estimation can effectively monitor the scope of environmental impact, guiding mining operations to prevent ecological damage. In this paper, we propose a dynamic boundary estimation approach for the suspended sediment plume, leveraging the sensing capability of the Autonomous Underwater Vehicles (AUVs). Based on the plume model and the point-by-point sensor measurements, a Luenberger-type observer is established for designing the AUV control algorithm. To address the challenge of unknown and time-varying environmental parameters, the estimation errors are reduced by using the projection modification unit. Rigorous convergence and stability analyses of the proposed control algorithm are provided by the Lyapunov method. Numerical simulations demonstrate that the improved algorithm enhances the estimation accuracy of unknown parameters and enables the AUV to patrol along the dynamic boundary in a shorter time, thereby verifying the effectiveness of the boundary estimation algorithm based on AUV sensing.
深海采矿过程中产生的悬浮泥沙羽状物对海洋环境和海底生态系统有显著影响。准确的边界估计可以有效监测环境影响范围，指导采矿作业以防止生态破坏。在本文中，我们利用自主水下航行器（AUV）的传感能力，提出了一种针对悬浮泥沙羽状物的动态边界估计方法。基于羽状物模型和逐点传感器测量，建立了一个Luenberger型观测器来设计AUV控制算法。为应对未知和时变环境参数的挑战，使用投影修正单元来减少估计误差。通过李雅普诺夫方法对所提出的控制算法进行了严格的收敛性和稳定性分析。数值模拟表明，改进后的算法提高了未知参数的估计精度，并使AUV能够在更短时间内沿动态边界巡逻，从而验证了基于AUV传感的边界估计算法的有效性。
