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:部署机器人在浅海水域时,波浪干扰可能会显著且动态地影响机器人的操作,特别是在靠近结构物的情况下出现问题。当前的控制策略存在关键限制,限制了亚海底航行器的部署范围。为了改善动态控制并提供更高的鲁棒性,本文提出一种串级比例微分(C-PD)和前馈(FF)控制方案用于干扰抵消,探索明确使用干扰估计来对抗状态扰动的概念。结果表明,与标准的 C-PD 控制器相比,所提出的控制器具有更高的性能,在各种海况下平均减少了约 48% 的波动。附加分析还研究了考虑粗略估计带来的不准确性时的性能,平均改进约为 17%,证明了所提出的策略处理这些不确定性的有效性。如果与足够的传感器、状态估计技术和预测算法结合使用,利用前馈干扰补偿控制行动为为改善波动引起的干扰下的车辆控制提供了潜在的解决方案。