THESIS
2017
ix, 60 pages : illustrations (some color) ; 30 cm
Abstract
One of the many lucrative marine industries is recreational SCUBA diving,
having contributed over US$ 10 billion to the US gross product in 2011 alone. However,
SCUBA diving is associated with a lot of potential hazards and inconveniences,
hence it opens up the need for an autonomous diving assistant.
The main aim of this project is to develop a mathematical model of an underwater
vehicle, and based on the model design a controller that can maintain leveled
flight during horizontal plane motion, while performing well in station-keeping operation.
This is challenging because underwater vehicle performance suffers a lot in
underwater environment where environmental disturbances are imminent and usually
unpredictable.
The mathematical model of the vehicle is developed by using sy...[
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One of the many lucrative marine industries is recreational SCUBA diving,
having contributed over US$ 10 billion to the US gross product in 2011 alone. However,
SCUBA diving is associated with a lot of potential hazards and inconveniences,
hence it opens up the need for an autonomous diving assistant.
The main aim of this project is to develop a mathematical model of an underwater
vehicle, and based on the model design a controller that can maintain leveled
flight during horizontal plane motion, while performing well in station-keeping operation.
This is challenging because underwater vehicle performance suffers a lot in
underwater environment where environmental disturbances are imminent and usually
unpredictable.
The mathematical model of the vehicle is developed by using system identification
method. This method finds the model that suits the underwater vehicle by
comparing the measured controller response with simulated controller response. The
model is validated by applying it to controllers with different gains. Rough model
can be generated, and it is possible to describe the controller performance, but is not
accurate enough to describe minute oscillations and vibrations.
Computed-torque controller is chosen due to the implementation simplicity
and robustness. However, the controller’s gain cannot be tuned to accomodate various
conditions, hence a fuzzy adaptive compensating controller is developed to complement
it. The fuzzy adaptive controller takes force input in x and y direction and generate
suitable output to compensate for horizontal plane motion. The implementation
of the compensating controller improves the controller performance by over 60% in
the same experiment.
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