Robust Disturbance Observer for Autonomous Surface Vessels Considering Model and Measurement Uncertainties

Core Concepts

A robust nonlinear disturbance observer is proposed to reconstruct the forces on an autonomous surface vessel impacted by environmental disturbances, even in the presence of model and measurement uncertainties.

Abstract

The paper presents a robust disturbance observer framework for maritime autonomous surface vessels that considers model and measurement uncertainties. The key contributions are: A nonlinear disturbance observer is designed to reconstruct the forces on a vessel impacted by the environment, including wind, waves, and sea currents. The observer achieves globally exponentially stable error dynamics, allowing the disturbances to be estimated even if they are highly dynamic. An unscented Kalman filter (UKF) is used to generate reliable state estimations from noisy measurements. A noise estimator is also introduced to approximate the noise strength and trigger a cascaded filtering mechanism when the measurement noise is severe. The observer framework can handle various uncertainties, including low measurement sampling rates, erroneous models, and noisy measurements. Simulations demonstrate the observer's capability to approximate dynamical environmental forces on a vessel despite these challenges. The proposed framework addresses key limitations of previous works by considering both model and measurement uncertainties, and providing a synchronized adaptation of the observed disturbances.

Stats

The vessel's mass matrix M is defined as a generalized, symmetric, positive definite matrix with five unknown parameters. The damping matrix D(ν) and Coriolis matrix C(ν) are highly nonlinear, with the entries defined by various hydrodynamic parameters. The environmental disturbances are simulated as dynamic forces induced by wind, waves, and sea currents.

Quotes

"Since measurements are affected by noise and physical models can be erroneous, an unscented Kalman filter (UKF) is used to generate more reliable state estimations." "Depending on the severity of the measurement noise, the observed disturbances are filtered through a cascaded structure consisting of a weighted moving average (WMA) filter, a UKF, and the proposed disturbance observer."

Key Insights Distilled From

An environmental disturbance observer framework for autonomous surface vessels

by Daniel Menge... at arxiv.org 03-29-2024

https://arxiv.org/pdf/2211.08360.pdf

Deeper Inquiries

How could the proposed disturbance observer framework be extended to handle more complex environmental conditions, such as rapidly changing wind patterns or unpredictable wave behavior?

The proposed disturbance observer framework could be extended to handle more complex environmental conditions by incorporating adaptive mechanisms that can adjust to rapidly changing wind patterns or unpredictable wave behavior. One approach could be to introduce adaptive learning algorithms, such as reinforcement learning or neural networks, to continuously update the observer's parameters based on real-time data. This would allow the observer to adapt to dynamic environmental conditions and improve its accuracy in estimating disturbances. Furthermore, the framework could be enhanced by integrating additional sensors or data sources to provide more comprehensive information about the environment. For example, including data from weather forecasting systems or oceanographic sensors could help the observer anticipate and respond to sudden changes in wind or wave patterns. By combining multiple sources of information, the observer can improve its ability to predict and mitigate the effects of complex environmental conditions.

What are the potential limitations of the Lyapunov-based stability analysis used in the observer design, and how could alternative approaches be explored to further strengthen the theoretical guarantees?

While Lyapunov-based stability analysis is a powerful tool for proving stability in control systems, it has some limitations that could affect the observer design. One limitation is that Lyapunov functions need to be carefully chosen to ensure convergence properties, and finding suitable Lyapunov functions can be challenging, especially in complex systems with nonlinear dynamics. To strengthen the theoretical guarantees of the observer design, alternative approaches could be explored. One approach is to use robust control techniques, such as H-infinity control or sliding mode control, which are more resilient to uncertainties and disturbances. These techniques can provide stronger guarantees of stability and performance in the presence of complex environmental conditions. Another approach is to incorporate model predictive control (MPC) into the observer design. MPC uses a predictive model of the system to optimize control actions over a finite time horizon, taking into account constraints and uncertainties. By integrating MPC with the disturbance observer, the system can proactively adjust its control strategy based on predicted disturbances, leading to improved performance and stability.

Could the disturbance observer be integrated with advanced control strategies to enable more robust and adaptive navigation of autonomous surface vessels in challenging maritime environments?

Yes, the disturbance observer can be integrated with advanced control strategies to enable more robust and adaptive navigation of autonomous surface vessels in challenging maritime environments. By combining the disturbance observer with advanced control techniques, such as model predictive control, adaptive control, or reinforcement learning, the vessel can enhance its situational awareness and responsiveness to environmental disturbances. For example, by integrating the disturbance observer with a model predictive controller, the vessel can anticipate and react to disturbances in real-time, adjusting its trajectory and control inputs to maintain stability and safety. The observer can provide accurate estimates of disturbances, which can then be used by the controller to optimize the vessel's path and behavior in dynamic and uncertain conditions. Furthermore, incorporating adaptive control strategies into the system can allow the vessel to learn and adapt to changing environmental conditions over time. By continuously updating its control policies based on feedback from the disturbance observer, the vessel can improve its performance and resilience in challenging maritime environments. Overall, integrating the disturbance observer with advanced control strategies can significantly enhance the navigation capabilities of autonomous surface vessels, enabling them to operate effectively and safely in complex and unpredictable maritime conditions.