Comparison of two PI methods applied to FDI on ship dynamics

Resumen

Most of non-linear type one and type two control systems suffers from lack ofdetectability when model based techniques are applied on fault detection andisolation (FDI) tasks. This research is centred on frequency techniques appliedto identify ship’s model parameters (PI) including non-structured or partiallyknown structured models using backpropagation neural networks as functionalapproximators. The results of the comparison of two strategies based in frequen-cy techniques are presented. Such frequency techniques are:— Mapping the frequency response associated to system parameterswhen a closed loop controlled ship is excited by the well-known harmonic bal-ance test (HBT).— Mapping the frequency response associated to system parameterswhen closed loop controlled ship is excited by a group of sinusoidal inputs addedto the manipulated variable (CLFRT).With achieved frequency response mappings, system parameters areassociated by means of functional approximation techniques. In this case, Feed-forward neural networks trained with backpropagation conjugate gradient algo-rithm are massively used. Finally, PI results are used in FDI tasks, where nomi-nal plant parameters are matched against on-line estimated parameters on a par-ity space approach.