%0 Journal Article
%T Recursive Subspace Identification of AUV Dynamic Model under General Noise Assumption
%A Zheping Yan
%A Di Wu
%A Jiajia Zhou
%A Lichao Hao
%J Mathematical Problems in Engineering
%D 2014
%I Hindawi Publishing Corporation
%R 10.1155/2014/547539
%X A recursive subspace identification algorithm for autonomous underwater vehicles (AUVs) is proposed in this paper. Due to the advantages at handling nonlinearities and couplings, the AUV model investigated here is for the first time constructed as a Hammerstein model with nonlinear feedback in the linear part. To better take the environment and sensor noises into consideration, the identification problem is concerned as an errors-in-variables (EIV) one which means that the identification procedure is under general noise assumption. In order to make the algorithm recursively, propagator method (PM) based subspace approach is extended into EIV framework to form the recursive identification method called PM-EIV algorithm. With several identification experiments carried out by the AUV simulation platform, the proposed algorithm demonstrates its effectiveness and feasibility. 1. Introduction In recent years, autonomous underwater vehicles (AUVs) have attracted increasing attentions due to their remarkable features such as high agility, excellent convenience and low cost in applications of underwater explorations and developments. However, contradictions lay between more and more complicated missions for AUV and the control and navigation systems that are not accurate enough. System identification methods have provided an alternative way other than traditional expensive instruments dependent approaches to improve the abilities of autonomous systems in various aspects [1¨C5]. As a result, a variety of researches have been put forward to identify the ordinary differential model of AUVs for model based control and navigation. Rentschler and coworkers [1] have demonstrated an iterative procedure to revise the model and controller of Odyssey III AUV to obtain better flight performances. Nonlinear observers based identification algorithm with sliding mode observer and EKF is also proposed for designation of nonlinear controller in [2]. For a more robust navigation system in case of sensor fault, Hegrenaes and Hallingstad [3] have used least squares algorithm to estimate both sea current disturbances and model parameters of the HUGIN 4500 AUV. However, due to the complexity of AUV system, many nonlinearities and coupled terms exist in ordinary differential equations that make the identification of whole pack of hydrodynamic coefficients quite complicated and time consuming. As a consequence, AUV differential model usually need to be simplified through eliminations of nonlinear and coupled terms before identification process. For example, in the research of Tiano et
%U http://www.hindawi.com/journals/mpe/2014/547539/