Abstract
To predict the manoeuvrability of unmanned marine vehicle and improve its manoeuvrability, the parameters of the manoeuvring model of unmanned marine vehicle need to be obtained. Aiming at the inconvenience of obtaining model parameters under the traditional experimental method, this article studies the parameter identification of unmanned marine vehicle’s manoeuvring model based on extended Kalman filter and support vector machine. Firstly, the second-order nonlinear manoeuvring response model of unmanned marine vehicle is discretized by the difference method, and the corresponding data are collected by the manoeuvring motion simulation of the response model. Secondly, the discrete response model is transformed into an augmented state vector based on extended Kalman filter, and the optimal estimation of the state vector is calculated to identify the parameters. And then, the discrete response model is transformed into a support vector machine-based regression model, the collected data are processed and a set of support vectors are obtained to further identify the parameters of the response model. Finally, by comparing the simulation experiments’ results from the original model and the identification model, the recognition results-based extended Kalman filter and support vector machine are analysed and some research results are obtained. The results of this article will provide a powerful reference for the design of unmanned marine vehicle’s motion control algorithm.
Highlights
Hydrodynamic coefficients of an AUV are estimated by Mohammad et al, the performance of two recursive state estimation algorithms, extended Kalman filters (EKF) and unscented Kalman filters (UKF), is explored for hydrodynamic coefficient estimation, and the results indicate that the UKF coefficient estimation method is faster than the EKF in terms of convergence time.[15]
The results indicate that the transformed unscented Kalman filters (TUKF) identifies the best hydrodynamic model due to solving both the cubature Kalman filters (CKF) non-local sampling problem and the EKF linearization problem.[22]
Parameter identification of Unmanned marine vehicle (UMV)’s manoeuvring model based on EKF and support vector machine (SVM) is addressed in this article
Summary
Unmanned marine vehicle (UMV), usually contained unmanned/autonomous underwater vehicle (UUV/AUV) and unmanned surface vessel (USV), is attracting more and more attention from researchers all over the world because of its extensive applications in military reconnaissance, homeland security, shallow-water surveys, environmental monitoring and coordinating with other unmanned system.[1,2,3] much advancement have been realized in this area, the demand for more advanced navigation,. The main methods of obtaining UMV model parameters are numerical calculation and model test. On the basis of the above researchers, this article presents an EKF and support vector machine (SVM) method for identifying the course manoeuvrability response parameters of UMV, and the advantages and disadvantages of the two methods in the course manoeuvrability response model of UMV are analysed, which provides a reliable method for the parameter identification of the course manoeuvrability model of UMV. The identified results of response model parameters based on EKF and SVM are compared with the original parameters, and the generalization ability of the two identification models is analysed in ‘Comparison and analysis of identification results’ section. Do some manoeuvring motion experiment and collect the experimental data, include the heading angle and rudder angle, process these data and use system identification algorithms to identify the parameters of the model.
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