针对水流干扰下的欠驱动水下机器人，提出一种观测器辅助的轨迹跟踪控制方法。
Observer-Assisted Relative-Velocity Compensation with LPV-$H_\infty$ Robust Correction for 3D Trajectory Tracking of Underactuated Non-Minimum-Phase AUVs under Ocean Currents
- 设计三阶段观测器估计相对速度，用于前馈补偿和鲁棒校正
- 实测显示水流估计误差降低89%-96%，轨迹误差降至0.24米
- 适合复杂海洋环境下欠驱动水下航行器的高精度路径跟踪
本文针对受未知洋流影响的鱼雷型欠驱动非最小相位水下机器人（AUV），提出一种观测器辅助的三维轨迹跟踪控制架构。通过三阶段状态-洋流观测器，获得相对速度估计值，分别供给非线性前馈项以主导抑制洋流扰动，以及供给基于LMI认证的LPV-H∞校正层。反馈线性化消除输入矩阵的非线性耦合，实现无交叉项的凸优化设计。残差层面的平衡定律表明，有效纵向前扰动依赖于洋流估计误差；奇异摄动分析证明了嵌入式LPV模型的局部实用一致最终有界性。在REMUS仿真中，覆盖三条轨迹与四种洋流场景，结果显示洋流估计误差减少89%-96%，平移残差降低约99%，均方根轨迹误差由4.04米降至0.24米。
原文摘要 · Abstract (English)
This paper develops an observer-assisted control architecture for 3D trajectory tracking of torpedo-type underactuated AUVs with non-minimum-phase sway/heave dynamics under unknown ocean currents. A three-stage state-current observer provides relative-velocity estimates to a nonlinear feedforward term for dominant current rejection and to an LMI-certified LPV-$\mathcal{H}_\infty$ correction layer. Feedback-linearising cancellation yields a constant input matrix, enabling convex synthesis without pairwise cross terms. A residual-level break-even law shows that the effective surge disturbance depends on current-estimation error, while a singular-perturbation analysis proves local practical uniform ultimate boundedness on the embedded LPV model. REMUS simulations over three trajectories and four current scenarios show 89-96% current-estimation reduction, about 99% translational residual reduction, and RMS tracking-error reduction from 4.04 m to 0.24 m.
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