Model-based many-objective optimization for control parameters of underwater glider considering long-term high-quality CTD measurements

Currently, the optimization of underwater gliders (UGs) primarily focuses on improving the platform performance, while ignoring the incidental influence on onboard observations. In this study, a model-based manyobjective optimization framework is first introduced to acquire the optimal control parameters of UGs, which enables efficient energy utilization and quality-assured conductivity, temperature, and depth (CTD) observations. Different from prior studies only emphasizing platform performance, our framework innovatively takes both CTD measurement errors and sampling errors into the optimization of control parameters, presenting a novel standpoint for achieving energy-efficient observations of interest within the dynamic ocean system. To further improve optimization in local environments, the dynamic model of UG in our framework was also upgraded through the integration of near real-time environmental information, facilitated by the Douglas-Peucker algorithm. Within the context of this framework, we investigated the requirements for conducting CTD observations in the forward and depth directions of glider movement and analyzed the optimal control parameters settings for these scenarios, encompassing both regular and segmented control strategies. The results suggest that conducting CTD observations during UG's climbing phase, rather than its diving phase, yields advantages. Otherwise, upgrading the buoyancy system and implementing segmented control strategy would be necessary. The thorough assessment in our study demonstrates the potential and versatility of the proposed framework in providing the optimal control parameters options for enhancing long-term high-quality CTD measurement by UGs. We believe that it will provide valuable guidance for glider operation in future field experiments.