Control Strategies Robust to Configurational Changes in Unmanned Underwater Vehicles.

Abstract

This research addresses reliability and robustness issues in the control of unmanned underwater vehicles (UUVs) with focus on vehicles of interest to the Navy. The central goal is to study and develop the means for a UUV to compensate for both disruptive configurational changes such as the failure of an actuator as well as more gentle changes such as variations in vehicle parameters that may result, for example, from addition/release of equipment on-board, from motion or action of a robotic manipulator or simply from parameter uncertainty. An important objective in support of the central goal is to use nonlinear methods to understand how to exploit nonlinear structure of UUV dynamics to advantage in control design. For example, it is often the case that one can show that a UUV is still controllable after an actuator failure. However, it is nonlinear methods that are required to verify this and it is nonlinearities in the UUV model that allow for the possibility of completing desired UUV motions after an actuator failure. Accordingly, it is nonlinear methods that should be used to develop algorithms that drive a UUV in the event of an actuator failure.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1999
Accession Number
ADA361486

Entities

People

  • Naomi Ehrich Leonard

Organizations

  • Princeton University

Tags

Communities of Interest

  • Autonomy
  • Ground and Sea Platforms
  • Space
  • Weapons Technologies

DTIC Thesaurus Topics

  • Actuators
  • Algorithms
  • Autonomous Underwater Vehicles
  • Control Systems
  • Deflection
  • Differential Geometry
  • Engineering
  • Lie Groups
  • Platforms
  • Saturation
  • Simulations
  • Simulators
  • Three Dimensional
  • Underwater Vehicles
  • Unmanned Underwater Vehicles
  • Vehicles

Readers

  • Robotics and Automation.
  • Systems Analysis and Design
  • Unmanned Aerial System (UAS) Autonomous Capabilities and Mission Reconnaissance.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Bayesian Inference
  • AI & ML - DoD AI Strategy
  • AI & ML - Machine Learning Algorithms
  • Autonomy
  • Autonomy - Autonomous System Control