Black-Box Marine Vehicle Identification with Regression Techniques for Random Manoeuvres

As a critical step to efficiently design control structures, system identification is concerned with building models of dynamical systems from observed input–output data. In this paper, a number of regression techniques are used for black-box marine system identification of a scale ship. Unlike othe...

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Bibliographic Details
Published in:Electronics (Basel) 2019-05, Vol.8 (5), p.492
Main Authors: Moreno, Raul, Moreno-Salinas, David, Aranda, Joaquin
Format: Article
Language:English
Subjects:
Art exhibits
Closed loop systems
Datasets
Design
Experiments
Identification
Kalman filters
Machine learning
Mathematical models
Regression
Robustness (mathematics)
Simulation
Support vector machines
System identification
Time series
Vehicle identification
Vehicles
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Summary:As a critical step to efficiently design control structures, system identification is concerned with building models of dynamical systems from observed input–output data. In this paper, a number of regression techniques are used for black-box marine system identification of a scale ship. Unlike other works that train the models using specific manoeuvres, in this work the data have been collected from several random manoeuvres and trajectories. Therefore, the aim is to develop general and robust mathematical models using real experimental data from random movements. The techniques used in this work are ridge, kernel ridge and symbolic regression, and the results show that machine learning techniques are robust approaches to model surface marine vehicles, even providing interpretable results in closed form equations using techniques such as symbolic regression.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics8050492