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Fin actuators can be used for for both thrust generation and vectoring.Therefore, fin-driven autonomous underwater vehicles (AUVs) can achieve highmaneuverability with a smaller number of actuators, but their control ischallenging. This study proposes an analytic control allocation method forunderactuated Autonomous Underwater Vehicles (AUVs). By integrating an adaptivehybrid feedback controller, we enable an AUV with 4 actuators to move in 6degrees of freedom (DOF) in simulation and up to 5-DOF in real-worldexperiments. The proposed method outperformed state-of-the-art controlallocation techniques in 6-DOF trajectory tracking simulations, exhibitingcentimeter-scale accuracy and higher energy and computational efficiency.Real-world pool experiments confirmed the method's robustness and efficacy intracking complex 3D trajectories, with significant computational efficiencygains 0.007 (ms) vs. 22.28 (ms). Our method offers a balance betweenperformance, energy efficiency, and computational efficiency, showcasing apotential avenue for more effective tracking of a large number of DOF forunder-actuated underwater robots.

Efficient Control Allocation and 3D Trajectory Tracking of a Highly Manoeuvrable Under-actuated Bio-inspired AUV