在复杂海流干扰情况下，针对欠驱动自主水下机器人（AUV）运动控制的鲁棒性问题，本文提出了一种新的自适应反步控制策略（IABC）。该策略的核心在于构建一个高效的海流干扰估计模块，该模块能够实时在线辨识海流动态，为控制器提供即时的补偿信号。鉴于传统反步控制方法（BC）在处理复杂系统时易出现的“微分爆炸”难题，设计一阶滤波器生成控制指令所需的虚拟导数，保证控制系统的稳定性和平滑性。仿真结果表明，在航向和深度控制上，IABC方法均展现出了更高的控制精度与更强的抗干扰能力。在时变海流场景下，IABC航向误差降低了32.9%，深度误差降低了36.2%。在随机海流场景下，IABC航向误差降低了3.5%，深度误差降低了5%。
In complex ocean current disturbances, to address the robustness issues of motion control for underactuated autonomous underwater vehicles (AUVs), this paper proposes a new adaptive integral backstepping control strategy (IABC). The core of this strategy involves building an efficient ocean current disturbance estimation module, which can identify ocean current dynamics in real-time and provide immediate compensation signals to the controller. Given the ‘differential explosion’ problem commonly encountered in traditional backstepping control methods (BC) when handling complex systems, a first-order filter was designed to generate the virtual derivatives required for control commands, ensuring the stability and smoothness of the control system. Finally, simulation results show that the IABC method exhibits higher control accuracy and stronger disturbance rejection capabilities in heading and depth control. In time-varying ocean current scenarios, the heading error of IABC decreased by 32.9%, and the depth error decreased by 36.2%. In random ocean current scenarios, the heading error of IABC decreased by 3.5%, and the depth error decreased by 5%.
2025,47(5): 138-145 收稿日期：2024-9-27
DOI：10.3404/j.issn.1672-7649.2025.05.021
分类号：U674.91；TP273
基金项目：2021年度湛江市促进经济高质量发展专项（060302072202）；2023广东省普通高校重点领域专项（A21705）；海洋防务技术创新中心创新基金（JJ-2023-715-01）
作者简介：陈庆东（1999 – ），男，硕士研究生，研究方向为水下机器人运动控制
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