When deploying robots in shallow ocean waters, wave disturbances can be significant, highly dynamic and pose problems when operating near structures; this is a key limitation of current control strategies, restricting the range of conditions in which subsea vehicles can be deployed. To improve dynamic control and offer a higher level of robustness, this work proposes a Cascaded Proportional-Derivative (C-PD) with Feed-forward (FF) control scheme for disturbance mitigation, exploring the concept of explicitly using disturbance estimations to counteract state perturbations. Results demonstrate that the proposed controller is capable of higher performance in contrast to a standard C-PD controller, with an average reduction of ~48% witnessed across various sea states. Additional analysis also investigated performance when considering coarse estimations featuring inaccuracies; average improvements of ~17% demonstrate the effectiveness of the proposed strategy to handle these uncertainties. The proposal in this work shows promise for improved control without a drastic increase in required computing power; if coupled with sufficient sensors, state estimation techniques and prediction algorithms, utilising feed-forward compensating control actions offers a potential solution to improve vehicle control under wave-induced disturbances.

翻译：在浅海海域部署机器人时，波浪扰动可能显著且高度动态，并在近结构物作业时引发问题；这是当前控制策略的关键局限，限制了水下运载器可部署的环境条件范围。为提升动态控制能力并增强鲁棒性，本文提出一种级联比例-微分（C-PD）结合前馈（FF）的控制方案用于扰动抑制，探索利用显式扰动估计来抵消状态扰动的概念。结果表明，与标准C-PD控制器相比，所提控制器能够实现更高性能，在不同海况下平均降低约48%。进一步分析还考察了在存在不精确粗糙估计时的性能；平均约17%的改善证明了所提策略处理这些不确定性的有效性。本工作提出的方案在无需大幅增加计算能力的情况下展现出改进控制的潜力；若与充足的传感器、状态估计技术和预测算法相结合，利用前馈补偿控制作用可为改善波浪诱导扰动下的运载器控制提供潜在解决方案。