Application of neural networks to modelling, estimation and control

6 Application of neural networks to modelling, estimation and control 

An interesting and important feature of a neural network trained using baek-propa-gation is that no knowledge of the proeess it is being trained to emulate is required. Also, sinee they learn from experienee rather than programming, their use may be eonsidered to be a blaek box approaeh. 

Providing input/output data is available, a neural network may be used to model the dynamies of an unknown plant. There is no eonstraint as to whether the plant is linear or nonlinear, providing that the training data eovers the whole envelope of plant operation. 

Rieliter et al. (1997) used this teehnique to model the dynamie eharaeteristies of a ship. The vessel was based on the Mariner Hull and had a length of 161m and a displaeement of 17 000 tonnes. The training data was provided by a three degree-of-freedom (forward veloeity, or surge, lateral veloeity, or sway and turn, or 

The training file eonsisted of input data of the form Time elapsed t kT), Rudder angle 6(kT), Engine speed n(kT) with eorresponding output data Forward veloeity u kT), Lateral veloeity v(kT), Yaw-rate r kT). 

---