Efficient Control Allocation and 3D Trajectory Tracking of a Highly Manoeuvrable Under-actuated Bio-inspired AUV
About
Fin actuators can be used for for both thrust generation and vectoring. Therefore, fin-driven autonomous underwater vehicles (AUVs) can achieve high maneuverability with a smaller number of actuators, but their control is challenging. This study proposes an analytic control allocation method for underactuated Autonomous Underwater Vehicles (AUVs). By integrating an adaptive hybrid feedback controller, we enable an AUV with 4 actuators to move in 6 degrees of freedom (DOF) in simulation and up to 5-DOF in real-world experiments. The proposed method outperformed state-of-the-art control allocation techniques in 6-DOF trajectory tracking simulations, exhibiting centimeter-scale accuracy and higher energy and computational efficiency. Real-world pool experiments confirmed the method's robustness and efficacy in tracking complex 3D trajectories, with significant computational efficiency gains 0.007 (ms) vs. 22.28 (ms). Our method offers a balance between performance, energy efficiency, and computational efficiency, showcasing a potential avenue for more effective tracking of a large number of DOF for under-actuated underwater robots.