Multi-Factor Excitation-Based Data-Driven Approach for Wide-Speed-Range Modeling of Marine Vehicles

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Accurate modeling of marine vehicle dynamics under varying maneuvering conditions is essential for control and trajectory planning. For data-driven modeling of marine vehicles, there is a lack of effective experimental design and modeling strategies to capture dynamic behaviors across different speed regimes. This study proposes a systematic data-driven framework that integrates multi-factor excitation signal design to sufficiently stimulate representative motion patterns. Specifically, a Latin Hypercube Sampling (LHS)-based method is employed to generate diverse control inputs that span a wide range of propeller revolutions and rudder angles, thereby enriching the training data. A Long Short-Term Memory network is then adopted to capture both transient dynamics and long-term dependencies from time-series data. The framework is evaluated on three representative marine vehicles and benchmarked against conventional excitation strategies, including Stratified Sampling, Random Rudder Sampling at fixed speed, and standard Zigzag maneuvers. Experimental results demonstrate that the LHS-based approach effectively captures the dynamic behaviors of marine vehicles across a broad speed range, ensuring consistent modeling performance under varying maneuvering conditions.

Recommended citation: Xia, A., Wang, Z., Wang, A., & Hao, L. (2025). Multi-Factor Excitation-Based Data-Driven Approach for Wide-Speed-Range Modeling of Marine Vehicles. IFAC-PapersOnLine, 59(22), 663-668.

Recommended citation: Xia, A., Wang, Z., Wang, A., & Hao, L. (2025). Multi-Factor Excitation-Based Data-Driven Approach for Wide-Speed-Range Modeling of Marine Vehicles. IFAC-PapersOnLine, 59(22), 663-668.
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