Identification of a simplified AUV pitch axis model for control design: Theory and experiments

Petrich, Jan; Neu, Wayne L.; Stilwell, Daniel J.

doi:10.1109/oceans.2007.4449350

Cited by 22 publications

(15 citation statements)

References 8 publications

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“…The propeller of the AUV drove the flow through the PVG, which used the selfexcited oscillations of a collapsible tube to generate vortex rings. The AUV used in these experiments (Virginia Tech 475 AUV) had a hull diameter of 12 cm, a length of 1 m, and was capable of travelling at a nominal speed of 1.5 m s −1 (Petrich, Neu & Stilwell 2007;Gadre et al 2008;Petrich 2009;Petrich & Stilwell 2010). An umbilical cable was used to supply the AUV with power and to send control commands to the motor.…”

Section: Main Vehiclementioning

confidence: 99%

Optimal vortex formation in a self-propelled vehicle

Whittlesey

Dabiri

2013

J. Fluid Mech.

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Previous studies have shown that the formation of coherent vortex rings in the nearwake of a self-propelled vehicle can increase propulsive efficiency compared with a steady jet wake. The present study utilizes a self-propelled vehicle to explore the dependence of propulsive efficiency on the vortex ring characteristics. The maximum propulsive efficiency was observed to occur when vortex rings were formed of the largest physical size, just before the leading vortex ring would pinch off from its trailing jet. These experiments demonstrate the importance of vortex ring pinch off in self-propelled vehicles, where coflow modifies the vortex dynamics.Key words: biological fluid dynamics, propulsion, vortex dynamics IntroductionThe presence of coherent vortical structures in the near-wake of a jet, hereafter referred to as 'vortex-enhanced propulsion', has been studied extensively and shown to increase propulsive performance in self-propelled vehicles (Siekmann 1962;Weihs 1977;Müller et al. 2000a;Krueger 2001;Finley & Mohseni 2004;Choutapalli 2007;Bartol et al. 2008;Krieg & Mohseni 2008, 2013Moslemi & Krueger 2010Ruiz, Whittlesey & Dabiri 2011). Weihs (1977, using many assumptions, analytically predicted an increase of 50 % in the average thrust through the use of vortex-enhanced propulsion. Later experimental work by Krueger & Gharib (2005) measured increases of up to 90 % of the propulsive thrust by optimization of the vortex ring characteristics. Ruiz et al. (2011) extended the results from stationary nozzles to a self-propelled vehicle, showing that vortex-enhanced propulsion can yield up to a 50 % increase in the propulsive efficiency over a steady jet.The vortices formed during vortex-enhanced propulsion can be characterized using the vortex formation time,t GRS , defined aŝ

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Section: Main Vehiclementioning

confidence: 99%

Optimal vortex formation in a self-propelled vehicle

Whittlesey

Dabiri

2013

J. Fluid Mech.

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“…Dynamic modeling of the underwater vehicle can be found throughout the literature (Abkowitz, ; Fossen, ; Healey & Lienard, ). Here, we adopt the model developed by Petrich, Neu, and Stilwell (), in which the equations of motion are written in the stability‐axis frame of the AUV. This enables the hydrodynamic forces and moments to be more conveniently expressed.…”

Section: Auv Model—dive Planementioning

confidence: 99%

“…From Petrich et al. (), the equations of motion containing state vector

x = [V, α, q, θ]

and input

u = [δ, F_{t}]

can be written as

ET (boldx) \dot{boldx} = R (boldx) + F (boldx, boldu) .

…”

Section: Auv Model—dive Planementioning

confidence: 99%

Minimum Speed Seeking Control for Nonhovering Autonomous Underwater Vehicles

Eng

Teo

2015

J. Field Robotics

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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. C 2015 Wiley Periodicals, Inc.

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“…[7,9,10], studies for torpedo-shaped AUVs are limited. These AUV based studies are generally carried out in the horizontal plane for lateral subsystem [11,12] or the vertical plane for longitudinal subsystem respectively [13]. This study utilises the offline LS method to derive an experimentally validated state space model that is able to describe the manoeuvring characteristics of a torpedo shaped AUV for both steering and diving subsystems.…”

Section: Introductionmentioning

confidence: 99%

Least Squares Optimisation Algorithm Based System Identification of an Autonomous Underwater Vehicle

Tran¹,

Radeni²,

Nguyen³

et al. 2016

Tuyển Tập Công Trình HNKH Toàn Quốc Lần Thứ 3 Về Điều Khiển &Amp; Tự Động Hoá VCCA - 2015

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This paper presents the identification of mathematical models governing dynamics in both the vertical and horizontal planes for a axisymmetric, torpedo-shaped Gavia class Autonomous Underwater Vehicle (AUV), based on a least squares optimisation algorithm. Rather than using the least squares algorithm to roughly estimate the mathematical models in a fixed time period, a simulator is developed based on least squares optimisation algorithm with the goal of accurately predicting the system response over time starting from initial conditions. The general equations for six degrees of freedom motions are decoupled into non-interacting longitudinal and lateral subsystems in the form of linear state space models with unknown parameters. These unknown parameters are initially determined by applying a least squares algorithm for experimental data collected from the AUV's on-board sensors. The previously identified models are then optimised to form the simulator able to estimate the system response. The numerically simulated data from the simulator show a good agreement with the field measured data. The simulator provides a useful tool to examine the manoeuvrability of AUV. The verification process proved that the least squares algorithm could be utilised as an optimisation algorithm in the system identification of autonomous underwater vehicle.

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Identification of a simplified AUV pitch axis model for control design: Theory and experiments

Cited by 22 publications

References 8 publications

Optimal vortex formation in a self-propelled vehicle

Optimal vortex formation in a self-propelled vehicle

Minimum Speed Seeking Control for Nonhovering Autonomous Underwater Vehicles

Least Squares Optimisation Algorithm Based System Identification of an Autonomous Underwater Vehicle

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