Review of dynamic positioning control in maritime microgrid systems

For many offshore activities, including offshore oil and gas exploration and offshore wind farm construction, it is essential to keep the position and heading of the vessel stable. The dynamic positioning system is a progressive technology, which is extensively used in shipping and other maritime structures. To maintain the vessels or platforms from displacement, its thrusters are used automatically to control and stabilize the position and heading of vessels in sea state disturbances. The theory of dynamic positioning has been studied and developed in terms of control techniques to achieve greater accuracy and reduce ship movement caused by environmental disturbance for more than 30 years. This paper reviews the control strategies and architecture of the DPS in marine vessels. In addition, it suggests possible control principles and makes a comparison between the advantages and disadvantages of existing literature. Some details for future research on DP control challenges are discussed in this paper

Data-driven sea state estimation for vessels using multi-domain features from motion responses

Situation awareness is of great importance for autonomous ships. One key aspect is to estimate the sea state in a real-time manner. Considering the ship as a large wave buoy, the sea state can be estimated from motion responses without extra sensors installed. However, it is difficult to associate waves with ship motion through an explicit model since the hydrodynamic effect is hard to model. In this paper, a data-driven model is developed to estimate the sea state based on ship motion data. The ship motion response is analyzed through statistical, temporal, spectral, and wavelet analysis. Features from multi-domain are constructed and an ensemble machine learning model is established. Real-world data is collected from a research vessel operating on the west coast of Norway. Through the validation with the real-world data, the model shows promising performance in terms of significant wave height and peak period.acceptedVersio

An Uncertainty-aware Hybrid Approach for Sea State Estimation Using Ship Motion Responses

Situation awareness is essential for autonomous ships. One key aspect is to estimate the sea state in a real-time manner. Considering the ship as a large wave buoy, the sea state can be estimated from motion responses without extra sensors installed. This task is challenging since the relationship between the wave and the ship motion is hard to model. Existing methods include a wave buoyanalogy (WBA) method, which assumes linearity between wave and ship motion, and a machine learning (ML) approach. Since the data collected from a vessel in the real world is typically limited to a small range of sea states, the ML method might suffer from catastrophic failure when the encountered sea state is not in the training dataset. This paper proposes a hybrid approach that combined the two methods above. The ML method is compensated by the WBA method based on the uncertainty of estimation results and, thus, the catastrophic failure can be avoided. Real-world historical data from the Research Vessel (RV) Gunnerus are applied to validate the approach. Results show that the hybrid approach improves estimation accuracy.acceptedVersio

Supervisory Control of Line Breakage for Thruster-Assisted Position Mooring System

Thruster-assisted position mooring (TAPM) is an energy-efficient and reliable stationkeeping method for deep water structures. Mooring line breakage can significantly influence the control system, and ultimately reduce the reliability and safety during operation and production. Therefore, line break detection is a crucial issue for TAPM systems. Tension measurement units are useful tools to detect line failures. However, these units increase the building cost of the system, and in a large portion of existing units in operation line tension sensors are not installed. This paper presents a fault-tolerant control scheme based on estimator-based supervisory control methodology to detect and isolate a line failure with only position measurements. After detecting a line break, a supervisor switches automatically a new controller into the feedback loop to keep the vessel within the safety region. Numerical simulations are conducted to verify the performance of the proposed technique, for a turret-based mooring system.© 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. This is the authors’ accepted and refereed manuscript to the articl

Safe Maneuvering Near Offshore Installations: A New Algorithmic Tool

Maneuvers of human-operated and autonomous marine vessels in the safety zone of drilling rigs, wind farms and other installations present a risk of collision. This article proposes an algorithmic toolkit that ensures maneuver safety, taking into account the restrictions imposed by ship dynamics. The algorithms can be used for anomaly detection, decision making by a human operator or an unmanned vehicle guidance system. We also consider a response to failures in the vessel's control systems and emergency escape maneuvers. Data used by the algorithms come from the vessel's dynamic positioning control system and positional survey charts of the marine installations

An environmental disturbance observer framework for autonomous surface vessels

This paper proposes a robust disturbance observer framework for maritime autonomous surface vessels considering model and measurement uncertainties. The core contribution lies in a nonlinear disturbance observer, reconstructing the forces on a vessel impacted by the environment. For this purpose, mappings are found leading to synchronized global exponentially stable error dynamics. With the stability theory of Lyapunov, it is proven that the error converges exponentially into a ball, even if the disturbances are highly dynamic. 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. In addition, a noise estimator is introduced, which approximates the noise strength. 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. To investigate the capability of this observer framework, the environmental disturbances are simulated dynamically under consideration of different model and measurement uncertainties. It can be seen that the observer framework can approximate dynamical forces on a vessel impacted by the environment despite using a low measurement sampling rate, an erroneous model, and noisy measurements.publishedVersio