Model-free system identification of surface ships in waves via Hankel dynamic mode decomposition with control

2025-02-17

AI Summary

Modeling the motion of freely navigating vessels in irregular waves using physics-based equations remains challenging, particularly under data-limited conditions. To address this, this paper proposes a model-free, data-driven system identification and prediction framework. Leveraging hull states, historical wave height sequences, and rudder angle as inputs, it constructs a low-order dynamical model. A novel Bayesian Hankel-Dynamic Mode Decomposition with control (Hankel-DMDc) method is introduced, integrating delay embedding to enhance nonlinear representation and inherently quantifying parametric and predictive uncertainties. Validated under SS7-level beam–oblique irregular wave conditions, the method achieves high-accuracy motion prediction over 15 wave periods, with stable, non-decaying errors and low computational cost. This approach breaks away from traditional equation-dependent paradigms, offering a robust, data-driven foundation for ship design optimization and real-time navigation decision support.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Kernel Methods
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

This study introduces and compares the Hankel dynamic mode decomposition with control (Hankel-DMDc) and a novel Bayesian extension of Hankel-DMDc as model-free (i.e., data-driven and equation-free) approaches for system identification and prediction of free-running ship motions in irregular waves. The proposed DMDc methods create a reduced-order model using limited data from the system state and incoming wave elevation histories, with the latter and rudder angle serving as forcing inputs. The inclusion of delayed states of the system as additional dimensions per the Hankel-DMDc improves the representation of the underlying non-linear dynamics of the system by DMD. The approaches are statistically assessed using data from free-running simulations of a 5415M hull's course-keeping in irregular beam-quartering waves at sea state 7, a highly severe condition characterized by nonlinear responses near roll-resonance. The results demonstrate robust performance and remarkable computational efficiency. The results indicate that the proposed methods effectively identify the dynamic system in analysis. Furthermore, the Bayesian formulation incorporates uncertainty quantification and enhances prediction accuracy. Ship motions are predicted with good agreement with test data over a 15 encounter waves observation window. No significant accuracy degradation is noted along the test sequences, suggesting the method can support accurate and efficient maritime design and operational planning.

Problem

• Model-free identification of ship motions
• Hankel-DMDc with Bayesian extension
• Prediction in irregular waves with uncertainty quantification

Innovation

• Model-free system identification
• Hankel dynamic mode decomposition
• Bayesian uncertainty quantification