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

Optimal Control of Wave Energy Converters

In this dissertation, we address the optimal control of the Wave Energy Converters. The Wave Energy Converters introduced in this study can be categorized as the single body heaving device, the single body pitching device, the single body three degrees of freedoms device, and the Wave Energy Converters array. Different types of Wave Energy Converters are modeled mathematically, and different optimal controls are developed for them. The objective of the optimal controllers is to maximize the energy extraction with and without the motion and control constraints. The development of the unconstrained control is first introduced which includes the implementation of the Singular Arc control and the Simple Model Control. The constrained optimal control is then introduced which contains the Shape-based approach, Pseudospectral control, the Linear Quadratic Gaussian optimal control, and the Collective Control. The wave estimation is also discussed since it is required by the controllers. Several estimators are implemented, such as the Kalman Filter, the Extended Kalman Filter, and the Kalman-Consensus Filter. They can be applied for estimating the system states and the wave excitation force/wave excitation force field. Last, the controllers are validated with the Discrete Displacement Hydraulic system which is the Power Take-off unit of the Wave Energy Converter. The simulation results show that the proposed optimal controllers can maximize the energy absorption when the wave estimation is accurate. The performance of the unconstrained controllers is close to the theoretical maximum (Complex Conjugate Control). Furthermore, the energy extraction is optimized and the constraints are satisfied by applying the constrained controllers. However, when the proposed controllers are further validated with the hydraulic system, they extract less energy than a simple Proportional-derivative control. This indicates the dynamics of the Power take-off unit needs to be considered in designing the control to obtain the robustness

A survey on tidal analysis and forecasting methods for Tsunami detection

Accurate analysis and forecasting of tidal level are very important tasks for human activities in oceanic and coastal areas. They can be crucial in catastrophic situations like occurrences of Tsunamis in order to provide a rapid alerting to the human population involved and to save lives. Conventional tidal forecasting methods are based on harmonic analysis using the least squares method to determine harmonic parameters. However, a large number of parameters and long-term measured data are required for precise tidal level predictions with harmonic analysis. Furthermore, traditional harmonic methods rely on models based on the analysis of astronomical components and they can be inadequate when the contribution of non-astronomical components, such as the weather, is significant. Other alternative approaches have been developed in the literature in order to deal with these situations and provide predictions with the desired accuracy, with respect also to the length of the available tidal record. These methods include standard high or band pass filtering techniques, although the relatively deterministic character and large amplitude of tidal signals make special techniques, like artificial neural networks and wavelets transform analysis methods, more effective. This paper is intended to provide the communities of both researchers and practitioners with a broadly applicable, up to date coverage of tidal analysis and forecasting methodologies that have proven to be successful in a variety of circumstances, and that hold particular promise for success in the future. Classical and novel methods are reviewed in a systematic and consistent way, outlining their main concepts and components, similarities and differences, advantages and disadvantages

Fuzzy sliding mode control of an offshore container crane

© 2017 A fuzzy sliding mode control strategy for offshore container cranes is investigated in this study. The offshore operations of loading and unloading containers are performed between a mega container ship, called the mother ship, and a smaller ship, called the mobile harbor (MH), which is equipped with a container crane. The MH is used to transfer the containers, in the open sea, and deliver them to a conventional stevedoring port, thereby minimizing the port congestion and also eliminating the need of expanding outwards. The control objective during the loading and unloading process is to keep the payload in a desired tolerance in harsh conditions of the MH motion. The proposed control strategy combines a fuzzy sliding mode control law and a prediction algorithm based on Kalman filtering for the MH roll angle. Here, the sliding surface is designed to incorporate the desired trolley trajectory while suppressing the sway motion of the payload. To improve the control performance, the discontinuous gain of the sliding control is adjusted with fuzzy logic tuning schemes with respect to the sliding function and its rate of change. Chattering is further reduced by a saturation function. Simulation and experimental results are provided to verify the effectiveness of the proposed control system for offshore container cranes

Real-time Forecasting and Control for Oscillating Wave Energy Devices

Ocean wave energy represents a signicant resource of renewable energy and can make an important contribution to the development of a more sustainable solution in support of the contemporary society, which is becoming more and more energy intensive. A perspective is given on the benefits that wave energy can introduce, in terms of variability of the power supply, when combined with oshore wind. Despite its potential, however, the technology for the generation of electricity from ocean waves is not mature yet. In order to raise the economic performance of Wave energy converters (WECs), still far from being competitive, a large scope exists for the improvement of their capacity factor through more intelligent control systems. Most control solutions proposed in the literature, for the enhancement of the power absorption of WECs, are not implemented in practise because they require future knowledge of the wave elevation or wave excitation force. The non-causality of the unconstrained optimal conditions, termed complex-conjugate control, for the maximum wave energy absorption of WECs consisting of oscillating systems, is analysed. A link between fundamental properties of the radiation of the floating body and the prediction horizon required for an effective implementation of complex-conjugate control is identified. An extensive investigation of the problem of wave elevation and wave excitation force forecasting is then presented. The prediction is treated as a purely stochastic problem, where future values of the wave elevation or wave excitation force are estimated from past measurements at the device location only. The correlation of ocean waves, in fact, allows the achievement of accurate predictions for 1 or 2 wave periods into the future, with linear Autoregressive (AR) models. A relationship between predictability of the excitation force and excitation properties of the floating body is also identified. Finally, a controller for an oscillating wave energy device is developed. Based on the assumption that the excitation force is a narrow-banded harmonic process, the controller is effectively tuned through a single parameter of immediate physical meaning, for performance and motion constraint handling. The non-causality is removed by the parametrisation, the only input of the controller being an on-line estimate of the frequency and amplitude of the excitation force. Simulations in (synthetic and real) irregular waves demonstrate that the solution allows the achievement of levels of power capture that are very close to non-causal complex-conjugate control, in the unconstrained case, and Model predictive control (MPC), in the constrained case. In addition, the hierarchical structure of the proposed controller allows the treatment of the issue of robustness to model uncertainties in quite a straightforward and effective way

Inversion for subbottom sound velocity profiles in the deep and shallow ocean

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution February 2005This thesis investigates the application of acoustic measurements in the deep and shallow ocean to infer the sound velocity profile (svp) in the seabed. For the deep water ocean, an exact method based on the Gelfand-Levitan integral equation is evaluated. The input data is the complex plane-wave reflection coefficient estimated from measurements of acoustic pressure in water. We apply the method to experimental data and estimate both the reflection coefficient and the seabed svp. A rigorous inversion scheme is hence applied in a realistic problem. For the shallow ocean, an inverse eigenvalue technique is developed. The input data are the eigenvalues associated with propagating modes, measured as a function of source-receiver range. We investigate the estimation of eigenvalues from acoustic fields measured in laterally varying environments. We also investigate the errors associated with estimating varying modal eigenvalues, analogous to the estimation of time-varying frequencies in multicomponent signals, using time-varying autoregressive (TVAR) methods. We propose and analyze two AR sequential estimators, one for model coefficients, another for the zeros of the AR characteristic polynomial. The decimation of the pressure field defined in a discrete range grid is analyzed as a tool to improve AR estimation. The nonlinear eigenvalue inverse problem of estimating the svp from a sequence of eigenvalues is solved by iterating linearized approximations. The solution to the inverse problem is proposed in the form of a Kalman filter. The resolution and variance of the eigenvalue inverse problem are analyzed in terms of the Cramer-Rao lower bound and the Backus-Gilbert (BG) resolution theory. BG theory is applied to the design of shallow-water experiments. A method is developed to compensate for the Doppler deviation observed in experiments with moving sources.I am grateful for the support of my work provided by the WHOI Academic Programs Office and the Office of Naval Research

Robust Multi-sensor Data Fusion for Practical Unmanned Surface Vehicles (USVs) Navigation

The development of practical Unmanned Surface Vehicles (USVs) are attracting increasing attention driven by their assorted military and commercial application potential. However, addressing the uncertainties presented in practical navigational sensor measurements of an USV in maritime environment remain the main challenge of the development. This research aims to develop a multi-sensor data fusion system to autonomously provide an USV reliable navigational information on its own positions and headings as well as to detect dynamic target ships in the surrounding environment in a holistic fashion. A multi-sensor data fusion algorithm based on Unscented Kalman Filter (UKF) has been developed to generate more accurate estimations of USV’s navigational data considering practical environmental disturbances. A novel covariance matching adaptive estimation algorithm has been proposed to deal with the issues caused by unknown and varying sensor noise in practice to improve system robustness. Certain measures have been designed to determine the system reliability numerically, to recover USV trajectory during short term sensor signal loss, and to autonomously detect and discard permanently malfunctioned sensors, and thereby enabling potential sensor faults tolerance. The performance of the algorithms have been assessed by carrying out theoretical simulations as well as using experimental data collected from a real-world USV projected collaborated with Plymouth University. To increase the degree of autonomy of USVs in perceiving surrounding environments, target detection and prediction algorithms using an Automatic Identification System (AIS) in conjunction with a marine radar have been proposed to provide full detections of multiple dynamic targets in a wider coverage range, remedying the narrow detection range and sensor uncertainties of the AIS. The detection algorithms have been validated in simulations using practical environments with water current effects. The performance of developed multi-senor data fusion system in providing reliable navigational data and perceiving surrounding environment for USV navigation have been comprehensively demonstrated

Predictor Design for Altitude Control of a Seaweed Harvester

In this paper, the predictor design, for altitude control of a seaweed harvester, is investigated. The harvesting system consists of a vessel and a suspended harvester device, the altitude of which is controlled by a winch. The control approach of Gallieri and Ringwood (2010), including a feedforward action, which requires a single step disturbance prediction, is investigated further, focusing on the disturbance prediction, for noisy sensors. The prediction is performed using AR and ARMA models, identified online, by using the Recursive Least Squared with Forgetting Factor (RLSFF) algorithm and the Kalman Filter (KF). The dependance between the error spectrum and the quality of the control is shown, and the prediction performances are evaluated, using an FFT-based criterion, oriented to the feedforward application. The control performances are then evaluated, and the results are compared to Gallieri and Ringwood (2010)