  The exploration of under-ice environments presents unique challenges due to
limited access for scientific research. This report investigates the potential
of deploying a fully actuated Remotely Operated Vehicle (ROV) for shallow area
exploration beneath ice sheets. Leveraging advancements in marine robotics
technology, ROVs offer a promising solution for extending human presence into
remote underwater locations. To enable successful under-ice exploration, the
ROV must follow precise trajectories for effective localization signal
reception. This study develops a multi-input-multi-output (MIMO) nonlinear
system controller, incorporating a Lyapunov-based stability guarantee and an
adaptation law to mitigate unknown environmental disturbances. Fuzzy logic is
employed to dynamically adjust adaptation rates, enhancing performance in
highly nonlinear ROV dynamic systems. Additionally, a Particle Swarm
Optimization (PSO) algorithm automates the tuning of controller parameters for
optimal trajectory tracking. The report details the ROV dynamic model, the
proposed control framework, and the PSO-based tuning process. Simulation-based
experiments validate the efficacy of the methodology, with experimental results
demonstrating superior trajectory tracking performance compared to baseline
controllers. This work contributes to the advancement of under-ice exploration
capabilities and sets the stage for future research in marine robotics and
autonomous underwater systems.
