The application of alternative modelling techniques to ROV dynamics

Simulation studies indicate that multi-input/multi-output recursive system identification (SI) and batch parameter estimation (PE) methods can be applied advantageously to the problem of modeling remotely operated underwater vehicles (ROV) dynamics. Least-squares (LS), extended-least-squares (ELS), and recursive prediction error (RPE) SI methods were used for SI. ELS produced slightly lower values of Akaike´s information criterion (AIC) than did LS, indicating that the estimation of the noise model C(z^-1) is worthwhile. RPE had relatively poor performance compared to LS and ELS. Statistical tests indicated that all of the nonlinear dynamics are conveniently lumped into low-order linear models. Real-time, recursive SI is a requirement for adaptive closed-loop control, where the performance of the ROV is estimated continuously and the controller gains are adjusted accordingly. Maximum-likelihood PE was used to produce a continuous LTI model of a ROV. Preliminary results are promising, although more work is required to define the estimated model structure