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Research Papers

Passivity-Based Stabilization of Underwater Gliders With a Control Surface

[+] Author and Article Information
Feitian Zhang

Collective Dynamics and Control Lab,
Department of Aerospace Engineering,
University of Maryland,
College Park, MD 20742
e-mail: fzhang17@umd.edu

Xiaobo Tan

Smart Microsystems Lab,
Department of Electrical
and Computer Engineering,
Michigan State University,
East Lansing, MI 48823
e-mail: xbtan@msu.edu

1This paper was written when Feitian Zhang was a Ph.D. student at Michigan State University.

2Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received April 27, 2014; final manuscript received November 5, 2014; published online January 27, 2015. Assoc. Editor: Hashem Ashrafiuon.

J. Dyn. Sys., Meas., Control 137(6), 061006 (Jun 01, 2015) (13 pages) Paper No: DS-14-1189; doi: 10.1115/1.4029078 History: Received April 27, 2014; Revised November 05, 2014; Online January 27, 2015

The problem of stabilizing steady gliding is critical for an underwater glider, which is subject to many non-negligible disturbances from the aquatic environment. In this paper, we propose a new systematic controller design and implementation approach for the stabilization problem, including a nonlinear, passivity-based controller and a nonlinear model-based observer, where the actuation is realized through a whale tail-like control surface. The controller is designed based on an approximation of a reduced model that is obtained through singular perturbation analysis, and consequently, it does not require full state feedback. The local stability of the full closed-loop system is established through linearization analysis. The nonlinear observer is designed to estimate the velocity-related system states, which are difficult to measure for such low-speed underwater vehicles. Simulation results are first provided to demonstrate that the proposed controller achieves rapid convergence in stabilization and the proposed observer has good performance especially in robustness against measurement noise. Experimental results using a gliding robotic fish are presented to support the effectiveness of both the controller and the observer.

Copyright © 2015 by ASME
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Figures

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Fig. 1

The schematic of an underwater glider with forces and moments defined in the corresponding coordinate frames (side view)

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Fig. 2

Simulation results on the trajectories of the gliding angle θg for the open-loop uδ = 0 and closed-loop (Kc = 2) cases, respectively

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Fig. 3

The trajectory of control uδ for the closed-loop simulation with different values for Kc

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Fig. 4

The trajectory of gliding angle θg for the closed-loop simulation with different values for Kc

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Fig. 5

Simulated trajectory of the measured pitch angle, which is corrupted with noise. The measurement noise is modeled as Gaussian random process with R = 0.1.

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Fig. 6

Simulation results: the trajectories of the gliding angle θg of the real state and nonlinear observer estimation with measurement noise.

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Fig. 7

A snapshot of the open-loop experiment with fixed tail angle using gliding robotic fish Grace in the lab tank

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Fig. 8

The trajectories of pitch angle of the on-board sensor reading, observer estimation, and computer-based simulation result, in the open-loop experiments using gliding robotic fish Grace. (a) δ = 15 deg; (b) δ = 30 deg; and (c) δ = 45 deg.

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Fig. 12

The trajectory of predefined tail angle in the experiment of gliding stabilization without feedback control

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Fig. 13

The trajectories of pitch angle and gliding angle in the passivity-based stabilization experiment with Kc = 3

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Fig. 14

The trajectory of tail angle in the passivity-based stabilization experiment with Kc = 3

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Fig. 15

The trajectories of pitch angle and gliding angle in the passivity-based stabilization experiment with Kc = 1

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Fig. 16

The trajectory of tail angle in the passivity-based stabilization experiment with Kc = 1

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Fig. 17

The trajectories in the comparative stabilization experiments with a proportional controller with KP = 2. (a) pitch angle and gliding angle and (b) tail angle.

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Fig. 18

The trajectories in the comparative stabilization experiments with a proportional controller with KP = 3. (a) pitch angle and gliding angle and (b) tail angle.

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Fig. 19

The trajectories in the comparative stabilization experiments with a PI controller with KP = 2 and KI = 1. (a) pitch angle and gliding angle and (b) tail angle

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Fig. 20

The trajectories in the comparative stabilization experiments with a PI controller with KP = 2 and KI = 3. (a) pitch angle and gliding angle and (b) tail angle.

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Fig. 11

The trajectories of pitch angle and gliding angle in the experiment of gliding stabilization without feedback control

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Fig. 10

A snapshot of stabilization experiment in the view of the 12 underwater cameras of the qualysis motion capture system in NBRF, University of Maryland

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Fig. 9

A snapshot of stabilization experiment using gliding robotic fish Grace in NBRF, University of Maryland

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