Abstract

Most autonomous underwater vehicles AUVs are propelled by a single thruster, use elevators and rudders as control surfaces, and are torpedo-shaped. Furthermore, they are positively buoyant to facilitate recovery during an emergency. For this class of nonhovering AUVs, there is a minimum speed at which the AUV must travel for stable depth control. Otherwise, the extra buoyancy will bring the AUV up to the surface when the fin loses its effectiveness at low speeds. Hence, we develop a novel algorithm such that the AUV is automatically controlled to travel at its minimum speed while maintaining a constant depth. This capability is important in a number of practical scenarios, including underwater loitering with minimum energy consumption, underwater docking with minimum impact, and high-resolution sensing at minimum speed. First, we construct a depth dynamic model to explain the mechanism of the minimum speed, and we show its relationship with the buoyancy, the righting moment, and the fin's effectiveness of the AUV. Next, we discuss the minimum speed seeking problem under the framework of extremum seeking. We extend the framework by introducing a new definition of steady-state mapping that imposes new structure on the seeking algorithm. The proposed algorithm employs a fuzzy inference system, which is driven by the real-time measurements of pitch error and elevator deflection. The effectiveness of the algorithm in seeking the minimum speed is validated in both simulations and field experiments.

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