Optimal fault detection and resolution during maneuvering for Autonomous Underwater Vehicles
Loading...
Authors
Gibbons, Andrew S.
Subjects
Autonomous Underwater Vehicles
Robotics
Robust Fault Detection
Extended Kalman Filtering
Optimization
Reliable Fault Sensitivity
Robotics
Robust Fault Detection
Extended Kalman Filtering
Optimization
Reliable Fault Sensitivity
Advisors
Healey, Anthony J.
Date of Issue
2000-03
Date
Publisher
Monterey, California. Naval Postgraduate School
Language
en_US
Abstract
In order to increase robustness, reliability, and mission success rate, autonomous vehicles must detect debilitating system control faults. Prior model-based observer design for 21UUV was analyzed using actual vehicle sensor data. It was shown, based on experimental response, that residual generation during maneuvering was too excessive to detect manually implemented faults. Optimization of vehicle hydrodynamic coefficients in the model significantly decreased maneuvering residuals, but did not allow for adequate fault detection. Kalman filtering techniques were used to improve residual reduction during maneuvering and increase residual generation during fault conditions. Optimization of the Kalman filter's system noise matrix, measurement noise matrix, and input gain scalar multiplier produced fault resolution which allowed for accurate detection of fault of relatively minor magnitude within minimal time constraints
Type
Thesis
Description
Series/Report No
Department
Organization
Identifiers
NPS Report Number
Sponsors
Funder
Format
xvi, 148 p.;28 cm.
Citation
Distribution Statement
Approved for public release; distribution is unlimited.