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Reducing actuator switchings for motion control of autonomous underwater vehicles

Chyba, Monique , Grammatico, Sergio , Huynh, Van T., Marriott, John , Piccoli, Benedetto , & Smith, Ryan N. (2013) Reducing actuator switchings for motion control of autonomous underwater vehicles. In Proceedings of the American Control Conference (ACC 2013), IEEE, Renaissance Washington, DC Downtown Hotel, Washington, D.C., pp. 1406-1411.

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    Abstract

    A priority when designing control strategies for autonomous underwater vehicles is to emphasize their cost of implementation on a real vehicle and at the same time to minimize a prescribed criterion such as time, energy, payload or combination of those. Indeed, the major issue is that due to the vehicles' design and the actuation modes usually under consideration for underwater platforms the number of actuator switchings must be kept to a small value to ensure feasibility and precision. This constraint is typically not verified by optimal trajectories which might not even be piecewise constants. Our goal is to provide a feasible trajectory that minimizes the number of switchings while maintaining some qualities of the desired trajectory, such as optimality with respect to a given criterion. The one-sided Lipschitz constant is used to derive theoretical estimates. The theory is illustrated on two examples, one is a fully actuated underwater vehicle capable of motion in six degrees-of-freedom and one is minimally actuated with control motions constrained to the vertical plane.

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    ID Code: 53801
    Item Type: Conference Paper
    Additional URLs:
    Keywords: actuator switching, optimal control, autonomous underwater vehicles, piece-wise constant strategy
    ISBN: 978-147990177-7
    ISSN: 0743-1619
    Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > APPLIED MATHEMATICS (010200) > Dynamical Systems in Applications (010204)
    Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Adaptive Agents and Intelligent Robotics (080101)
    Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > MARITIME ENGINEERING (091100) > Special Vehicles (091106)
    Divisions: Current > Schools > School of Electrical Engineering & Computer Science
    Current > QUT Faculties and Divisions > Science & Engineering Faculty
    Funding:
    • ONR/N62909-11-1-4046
    Copyright Owner: © 2013 AACC American Automatic Control Council.
    Deposited On: 24 Sep 2012 08:37
    Last Modified: 10 Apr 2014 17:47

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