Nominal Model-Based Backstepping and Sliding Mode Control for Deep-sea Mining Vehicle

rajectory tracking is a critical part for deep-sea mining vehicle (DSMV) operating in the seabed environment. Aiming at improving the accuracy together with the adaptability of the tracking controller, the paper proposes a control method combining the Extended Kalman Filter (EKF) and Nominal Model-Based Backstepping and Sliding Mode Control (NM-BSMC). The backstepping controller is designed for the nominal model, and the uncertain part of the actual system is compensated by the sliding mode controller, so that the robust control of the uncertain system can be achieved. In addition, EKF is used to estimate the road resistance coefficients, which strengthens the controller adaptability to uncertain conditions. The proposed method is verified by a hardware-in-the-loop experimental environment with seabed conditions. The experiment results show that the proposed NM-BSMC can adapt to the unstructured environment precisely and achieve a good tracking performance as well as strong robustness.

轨迹跟踪是在海底环境作业的深海采矿车（deep-sea mining vehicle, DSMV）的至关重要的组成部分。为提升轨迹跟踪控制器的精度与适配性，本文提出一种融合扩展卡尔曼滤波（Extended Kalman Filter, EKF）与基于标称模型的反演滑模控制（Nominal Model-Based Backstepping and Sliding Mode Control, NM-BSMC）的控制方案。该方法针对标称模型设计反演控制器，并通过滑模控制器补偿实际系统的不确定性分量，进而实现不确定系统的鲁棒控制。此外，本文利用EKF估计行驶阻力系数，进一步强化控制器对不确定工况的适应能力。本文通过搭建模拟海底环境的硬件在环实验平台对所提方法开展验证。实验结果表明，所提出的基于标称模型的反演滑模控制方法能够精准适配非结构化环境，同时实现优异的跟踪性能与较强的鲁棒性。

搜集汇总

背景与挑战

背景概述

该数据集聚焦深海采矿车辆的轨迹跟踪控制，提出了一种结合扩展卡尔曼滤波器和基于名义模型的反步滑模控制的方法，旨在提升跟踪精度和系统鲁棒性。通过硬件在环实验验证，该方法能适应非结构化海底环境，实现精确跟踪并表现出强适应性。