针对X舵自主水下航行器（Autonomous Underwater Vehicle，AUV）在未知海流干扰下横滚耦合效应影响与节能的多目标约束控制问题，提出一种改进鲸鱼优化算法，并基于此设计多目标约束鲸鱼非线性扩张状态观测器–线性二次型最优（Whale Extend State Observer–Linear Quadratic Regulator，WESO–LQR）控制器。首先，为了抑制横滚耦合效应，设计一种具备横滚控制能力的航向–深度线性二次型最优控制器（Linear Quadratic Regulator，LQR）；其次，针对环境中未知海流干扰和模型中的不确定未知项影响，引入非线性扩张状态观测器（Extend State Observer，ESO）对系统的未知项估计；最后，针对舵机频繁纠偏动作能耗加剧的问题，采用改进鲸鱼优化算法（Whale Optimization Algorithm，WOA）对控制器参数进行优化。仿真结果表明，具备横滚控制的LQR控制算法可将平均横滚角抑制在2.8°内；在未知海流干扰下，ESO–LQR相较于LQR，深度与航向跟踪误差分别降低19.3%与7.91%；WESO–LQR总舵机动作频次较非线性扩张状态观测器–线性二次型最优控制（ESO–LQR）减少约15.8%，评价指标降低了15.57%。结果表明，在未知海流干扰下，所提方法能够实现横滚耦合下的稳定控制，可降低系统能耗。
For the multi-objective constrained control problem of an X-rudder autonomous underwater vehicle (AUV) under unknown ocean current disturbances—addressing roll-coupling suppression and energy efficiency—we propose an improved Whale Optimization Algorithm (WOA) and design a Whale Extended State Observer-Linear Quadratic Regulator (WESO-LQR) controller. First, to suppress roll-coupling effects, we develop a heading-depth LQR with explicit roll-attenuation capability. Second, to estimate unknown ocean currents and model uncertainties, we integrate a nonlinear Extended State Observer (ESO). Third, to mitigate energy consumption from frequent actuator corrections, we employ the enhanced WOA to optimize controller gains. Simulation results show that the roll-capable LQR confines mean roll angle within 2.8°. Under unknown currents, ESO-LQR reduces depth and heading tracking errors by 19.3% and 7.91%, respectively, versus standard LQR. With parameter optimization by the improved WOA, WESO-LQR cuts total actuator movements by approximately 15.8% compared to ESO-LQR, and the evaluation index is reduced by 15.57%. These results demonstrate stable roll-coupled control and reduced energy use in unknown current environments.
2026,48(6): 159-167 收稿日期：2025-6-24
DOI：10.3404/j.issn.1672-7649.2026.06.021
分类号：U674.941
基金项目：广东省教育厅重点领域专项（2023ZDZX3004）；海洋防务创新基金项目（JJ–2023–715–01）
作者简介：钟耀鹏(2001-),男,硕士研究生,研究方向为水下机器人运动控制
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