Adaptive ship stabilization: Control of ship stabilizing actuators using modified deep model reference adaptive control
Typ
Examensarbete för masterexamen
Program
Publicerad
2022
Författare
Bertilsson, Tobias
Persson, Johannes
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
For common control methods, there are often an extensive modeling and tuning
procedure needed to obtain a good control performance. This thesis investigates
a way of omitting that procedure by utilizing a machine learning based control
method. An adaptive method is presented of how to control the stabilizing actuators
of a ship with limited or no information about the ship’s dynamics, and where the
core of the adaptiveness lies in the combination of a feature vector, extracted from
an artificial neural network (ANN), and a weight vector. The main objective was to
reduce the most critical ship movement, rolling motions, using active fin stabilizers.
The proposed adaptive controller showed promising results that increased the
performance of the ordinary linear controller, while also being able to adapt when
changes in the plant were introduced. The second objective was to see if the
proposed controller was capable of controlling multiple kinds of actuators. The
results indicate that this is the case, however, some additional modifications need
to be made to achieve the same performance as in the first case when using only
fins.
Beskrivning
Ämne/nyckelord
Adaptive control , Ship stabilization , Model reference adaptive control (MRAC) , Deep Model Reference Adaptive Control (DMRAC) , Machine learning , Online training