Comparison of two PI methods applied to FDI on ship dynamics

Abstract

Most of non-linear type one and type two control systems suffers from lack of detectability when model based techniques are applied on fault detection and isolation (FDI) tasks. This research is centred on frequency techniques applied to identify ship´s model parameters (PI) including non-structured or partially known structured models using backpropagation neural networks as functional approximators. The results of the comparison of two strategies based in frequency techniques are presented. Such frequency techniques are: -Mapping the frequency response associated to system parameters when a closed loop controlled ship is excited by the well-known harmonic balance test (HBT). -Mapping the frequency response associated to system parameters when closed loop controlled ship is excited by a group of sinusoidal inputs added to the manipulated variable (CLFRT). With achieved frequency response mappings, system parameters are associated by means of functional approximation techniques. In this case, Feedforward neural networks trained with backpropagation conjugate gradient algorithm are massively used. Finally, PI results are used in FDI tasks, where nominal plant parameters are matched against on-line estimated parameters on a parity space approach.