Models for Remotely Operated Underwater Vehicles (ROVs) are difficult to derive because their dynamics are strongly coupled, highly nonlinear and vary according to the vehicle’s operating configuration. In addition, conventional modeling techniques require the use of expensive, specialized testing equipment. An alternative procedure using System Identification (SI) to process data gathered during simple free-running trials can generate fast, inexpensive and accurate ROV models. Three SI algorithms, Least Squares (LS), Extended Least Squares (ELS) and the Recursive Prediction Error Method (RPEM), are tested on simulated ROV data and compared for accuracy and economy. The vehicle studied in this paper, the UMEL Seapup, can be represented accurately by a linear model at the low speeds where accurate maneuvering control is most important and gain scheduling can be used to switch models at higher speeds.
Recursive Prediction of Graph Signals With Incoming Nodes