Accurate and adaptive dynamic models are critical for underwater vehicle-manipulator systems where hydrodynamic effects induce time-varying parameters. This paper introduces a novel uncertainty-aware adaptive dynamics model framework that remains linear in lumped vehicle and manipulator parameters, and embeds convex physical consistency constraints during online estimation. Moving horizon estimation is used to stack horizon regressors, enforce realizable inertia, damping, friction, and hydrostatics, and quantify uncertainty from parameter evolution. Experiments on a BlueROV2 Heavy with a 4-DOF manipulator demonstrate rapid convergence and calibrated predictions. Manipulator fits achieve R2 = 0.88 to 0.98 with slopes near unity, while vehicle surge, heave, and roll are reproduced with good fidelity under stronger coupling and noise. Median solver time is approximately 0.023 s per update, confirming online feasibility. A comparison against a fixed parameter model shows consistent reductions in MAE and RMSE across degrees of freedom. Results indicate physically plausible parameters and confidence intervals with near 100% coverage, enabling reliable feedforward control and simulation in underwater environments.

翻译：精确且自适应的动力学模型对于水下航行器-机械臂系统至关重要，因为水动力效应会引发时变参数。本文提出一种新颖的不确定性感知自适应动力学模型框架，该框架在集总化的航行器与机械臂参数中保持线性，并在在线估计过程中嵌入凸物理一致性约束。采用移动水平估计方法堆叠水平回归器，强制执行可实现的惯性、阻尼、摩擦与静水力学特性，并从参数演化中量化不确定性。在搭载4自由度机械臂的BlueROV2 Heavy平台上进行的实验展示了快速收敛和经过校准的预测效果。机械臂拟合的R²达到0.88至0.98，斜率接近1；而在更强耦合和噪声条件下，航行器的纵荡、垂荡和横摇运动也得以高保真复现。求解器中位时间约为每次更新0.023秒，证实了在线可行性。与固定参数模型的对比显示，各自由度上的平均绝对误差和均方根误差均持续降低。结果表明，所获参数具有物理合理性，置信区间覆盖度接近100%，从而为水下环境中的可靠前馈控制与仿真提供了支撑。