AbstractThe design of automatic systems for steering a ship presents difficult challenges because of their dynamic properties which vary considerably within the range of sailing conditions. Automatic steering of ships has its origin at the beginning of the century and was prompted by the introduction of the gyrocompass. Until the earlier 70s almost all autopilots for a ship were based on the proportional-derivative-integral (PID) controller. The main disadvantage with PID controllers is that the optimal parameters setting can be achieved only for a particular sailing condition. This shortcoming was and is still dealt with in the framework of adaptive theory where the controller parameters are adjusted in the attempt to seek the optimum of a pre-set performance function. Despite such a potential advantage, at present adaptive control theory is limited to linear plants and requires a certain amount of a-priori information for a successful application.
This thesis is concerned with the applicability of intelligent control techniques to the problem of designing course-keeping and course-changing autopilots for ships. For this reason the framework of intelligent control theory is introduced and a pragmatic definition of intelligent controllers is stated. The learning and adaptive features of neural networks and fuzzy logic systems are exploited and used to solve advantageously the control design problem. Adaptive networks are used as a unifying structure where different kinds of neural networks and fuzzy logic paradigms can be described. In this framework, comparisons between neural networks and fuzzy logic systems are made and results from one field can be easily extended to the other.
Although the use of such systems for the design of autopilots is in its early stage, the majority of the contributions which have appeared in literature have focused on the use of feedforward networks trained with the back-propagation algorithm. The main contributions of this thesis are the critical analysis of the feedforward network controller trained with the back-propagation algorithm, the proposition of an alternative controller architecture based on the use of radial basis function networks and to give conditions under which the stability analysis of the intelligent controllers so designed can be evaluated.
|Date of Award||Oct 2000|
- Intelligent control
- Fuzzy Logic
- Automatic systems