Physics simulations are a computational bottleneck in computer-aided design
(CAD) optimization processes. Hence, in order to make accurate (computationally
expensive) simulations feasible for use in design optimization, one requires
either an optimization framework that is highly sample-efficient or fast
data-driven proxies (surrogate models) for long running simulations. In this
work, we leverage recent advances in optimization and artificial intelligence
(AI) to address both of these potential solutions, in the context of designing
an optimal unmanned underwater vehicle (UUV). We first investigate and compare
the sample efficiency and convergence behavior of different optimization
techniques with a standard computational fluid dynamics (CFD) solver in the
optimization loop. We then develop a deep neural network (DNN) based surrogate
model to approximate drag forces that would otherwise be computed via direct
numerical simulation with the CFD solver. The surrogate model is in turn used
in the optimization loop of the hull design. Our study finds that the Bayesian
Optimization Lower Condition Bound (BO LCB) algorithm is the most
sample-efficient optimization framework and has the best convergence behavior
of those considered. Subsequently, we show that our DNN-based surrogate model
predicts drag force on test data in tight agreement with CFD simulations, with
a mean absolute percentage error (MAPE) of 1.85%. Combining these results, we
demonstrate a two-orders-of-magnitude speedup (with comparable accuracy) for
the design optimization process when the surrogate model is used. To our
knowledge, this is the first study applying Bayesian optimization and DNN-based
surrogate modeling to the problem of UUV design optimization, and we share our
developments as open-source software.

本研究探讨了在无人水下器（UUV）的设计过程中，采用计算流体动力学（CFD）与基于深度神经网络（DNN）的代理模型相结合，通过贝叶斯优化（BO）实现高效样本及快速数据驱动的 UUV 设计优化问题，结果表明，BO LCB 算法是最具高效的优化框架，DNN 代理模型可以实现计算的极速提升与相关精度的优化。

水下航行器外壳的高效可替代设计优化

Sample-Efficient and Surrogate-Based Design Optimization of Underwater  Vehicle Hulls

Tracking control has been a vital research topic in robotics. This paper
presents a novel hybrid control strategy for an unmanned underwater vehicle
(UUV) based on a bioinspired neural dynamics model. An enhanced backstepping
kinematic control strategy is first developed to avoid sharp velocity jumps and
provides smooth velocity commands relative to conventional methods. Then, a
novel sliding mode control is proposed, which is capable of providing smooth
and continuous torque commands free from chattering. In comparative studies,
the proposed combined hybrid control strategy has ensured control signals
smoothness, which is critical in real world applications, especially for an
unmanned underwater vehicle that needs to operate in complex underwater
environments.

该研究论文提出了一种基于仿生神经动力学模型的新型混合控制策略，以提高无人水下车辆的跟踪控制，将改进后的后向运动学控制策略与新型滑模控制相结合，以实现控制信号的平滑性，这在无人水下车辆的实际应用中尤为重要。