Session: 06-05-04 Marine Hydrodynamics - IV

Submission Number: 180931

Modeling Environmental Disturbances and Control Response for Small Autonomous Marine Vehicles 

Autonomous control systems for marine vehicles have become essential for environmental monitoring, coastal research, and offshore operations. However, most existing hydrodynamic and control models are developed for large Unmanned Surface Vehicles (USVs) or conventional vessels. Most of these models cannot be directly applied to small USVs because the same environmental forces, particularly wind and waves, have a much greater impact on small USVs due to their lower mass, reduced inertia, and smaller hull geometry. As a result, small autonomous boats are more sensitive to environmental disturbances which leads to increased trajectory deviation and control instability when using models that are designed for larger craft. This creates a critical need for modeling frameworks specifically tailored to the dynamic behavior of small USVs where accurate representation of environmental forces is essential for effective control system design and validation. To address this gap, this paper presents a comprehensive modeling and simulation framework developed in MATLAB/Simulink to analyze the dynamic response of a small USV under environmental disturbances such as wind and wave forces. The framework integrates the vehicle’s surge, sway, and yaw dynamics with environmental disturbance models and a Proportional-Integral-Derivative (PID) control system to evaluate and validate the navigation performance in realistic marine conditions. The emphasis of this work is on reproducibility and modeling integrity rather than physical hardware which offers a flexible tool for researchers to test and refine control strategies for various small-scale USV configurations. Wind and wave forces are modeled as external disturbances acting on the vehicle’s motion equations, where wind is expressed as a directional vector that affects heading and velocity and wave effects are represented through sinusoidal functions corresponding to mild, moderate, and severe sea states. These forces are applied to a dynamic model that captures both translational and rotational behaviors to create a high-accuracy simulation of real ocean conditions. The PID controller continuously adjusts differential thrust between twin motors to correct positional errors and maintain course stability. Through iterative tuning of the proportional (K_p), integral (K_i), and derivative (K_d) gains, the controller achieves an optimal balance between rapid response and smooth correction minimizing cross-track deviation and overshoot. Three main simulation scenarios were evaluated: calm baseline conditions, wind-only disturbances, and combined wind-and-wave disturbances. Each simulation began with identical initial and target coordinates to ensure consistent comparison across conditions. Performance was assessed using quantitative metrics such as trajectory deviation, path length, and time to target. Results show that without control, wind and wave disturbances cause significant deviations from the desired path with errors increasing proportionally to disturbance magnitude. When the PID controller is engaged, trajectory deviation is reduced dramatically and positional accuracy is maintained within approximately one meter of the target even under high wind speeds and strong wave amplitudes. The modular structure also facilitates efficient post-processing and visualization which allows for detailed analysis of vehicle motion, control effort, and system stability. The results establish that a properly tuned PID controller significantly enhances navigational precision, reduces path error, and improves operational stability for small USVs in disturbed marine conditions. The presented framework provides a reliable and cost-effective foundation for the virtual testing of control algorithms before physical deployment which ultimately supports the development of safer, more efficient, and autonomous marine platforms. Future work will focus on integrating live sensor data, correlating simulation outcomes with experimental field trials, and extending the framework to include adaptive gain scheduling for rapidly changing sea states. By combining a rigorous dynamic model with a flexible control and disturbance architecture, this research contributes a valuable simulation tool for the continued advancement of autonomous surface vehicle technology and marine system control.

Presenting Author: Rikki Ramos Texas A&M University Kingsville

Presenting Author Biography: Rikki Ramos is a PhD student in Mechanical Engineering at Texas A&M University-Kingsville. With over a decade of professional experience in aviation maintenance and CNC machining at Corpus Christi Army Depot, she transitioned into academia and earned a Bachelor of Science in Mechanical Engineering (Summa Cum Laude) and a Master of Science in Industrial Engineering. Her research focuses on advanced unmanned systems, including the development of autonomous surface vehicles for oil spill detection. She is supported by prestigious awards like the TSGC Fellowship and the AIAA Southwest Texas Section Scholarship. Combining hands-on expertise with a commitment to innovation, Rikki aims to advance engineering practices for sustainable and efficient technologies.

Authors:

Rikki Ramos Texas A&M University Kingsville
Hua Li Texas A&M University Kingsville
Kai Jin Texas A&M University Kingsville
Oscar Garcia WaterMapping LLC