Modelling of a surface marine vehicle with kernel ridge regression confidence machine

• Published in: Applied soft computing 2019-03, Vol.76, p.237-250
• Main Authors: Moreno-Salinas, David, Moreno, Raul, Pereira, Augusto, Aranda, Joaquin, de la Cruz, Jesus M.
• Format: Article
• Language: English
• Subjects: Conformal predictors (CP); Kernel ridge regression (KRR); Kernel ridge regression confidence machine (KRRCM); Marine systems; System identification
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2018.12.002

This paper describes the use of Kernel Ridge Regression (KRR) and Kernel Ridge Regression Confidence Machine (KRRCM) for black box identification of a surface marine vehicle. Data for training and test have been obtained from several manoeuvres typically used for marine system identification. Thus, a 20/20 degrees Zig-Zag, a 10/10 degrees Zig-Zag, and different evolution circles have been employed for the computation and validation of the model. Results show that the application of conformal prediction provides an accurate model that reproduces with large accuracy the actual behaviour of the ship with confidence margins that ensure that the model response is within these margins, making it a suitable tool for system identification. •Black box identification based on Conformal Predictors is used for marine vehicles.•Classical manoeuvres for marine vehicle identification are used to collect data.•A continuous-time model is trained and tested using data from real experiments.•Modelling with Kernel Ridge Regression and Kernel Ridge Regression Confidence Machine.•A confidence margin is proposed where the real behaviour of the vehicle should lie in.