Dynamic Positioning Vessel Research Articles

Prescribed-time active fault-tolerant control of dynamic positioning vessels based on improved iterative learning observer

Considering the dynamic positioning (DP) vessel control system with actuator faults and unknown environment disturbances, a fault-tolerant control scheme based on improved iterative learning observer (ILO) is proposed in this article. Firstly, the kinematics and dynamics models of DP vessels are established containing the actuator faults and environmental disturbances. Secondly, an improved ILO is designed to handle the unknown faults and disturbances concurrently with higher estimation precision by introducing the state information of the previous moment. Further, a prescribed-time active fault-tolerant control scheme is presented on the basis of the improved ILO and prescribed-time stability. And the stability of proposed ILO and DP fault-tolerant control system are analyzed based on Lyapunov theory. Finally, the effectiveness and advantages of the presented ILO and active fault-tolerant control scheme are illustrated by numerical simulations and comparisons.

Анализ работы современных водолазных судов, оснащенных системой динамического позиционирования (DP)

. The principle of operation of modern diving vessels equipped with dynamic positioning (DP) systems is analyzed. The classification of diving operations on such vessels is considered. To perform certain construction work or maintenance of underwater facilities, a diving team is needed – the most important component in an integral part of the most difficult work, where the ROV (remote-controlled uninhabited underwater vehicle) of the working class, equipped with tools for certain purposes, is not adapted for complex tasks. The topic of limiting the length of the hose cable (a technology that ensures the delivery of vital consumables, signals and energy via cable to the diver) for underwater work is touched upon. An assessment of the safe working distance of divers near the bow thrusters and propeller columns of the DP vessel is carried out using the example of the most modern vessel in the Caspian Sea to support diving operations, as well as for the installation of underwater structures of the deep-sea bases of the Khankendi. Conclusions are drawn about the widespread use of DP systems on modern ships, in particular those involved in the oil and gas industry, as well as other areas of the marine industry. A study has been conducted to assess the safety of diving operations of a DP vessel operating in shallow water. An assessment is given to limit the movement of the vessel during diving operations. The specifics of the universal vessel involved in connection with the risks of human life, the working environment of divers, various environmental conditions, the proximity of the vessel's propulsion systems and many other additional risks that pose a danger are indicated. The thesis on the need for careful planning and management of diving operations has been confirmed.

Robust adaptive tracking control for dynamic positioning ships subject to dynamic safety constraints and actuator saturation

This paper proposed a novel robust adaptive trajectory tracking control scheme for dynamic positioning (DP) ships subjected to unknown time-varying disturbances, unknown model parameters, dynamic safety constraints, and actuator saturation. The dynamic safety constraints were guaranteed based on the tan-type barrier Lyapunov function (BLF) technique, which ensured the position and velocity of the DP vessel within the given constraints. Moreover, the radial basis function neural network (RBFNN) was adopted to approximate the unknown parameters of the dynamics model, and an adaptive neural disturbance observer (ANDO) was designed to compensate for lumped disturbances. An anti-windup system was developed to handle the actuator saturation effect. By combining the above techniques and back-stepping method, the final control law was presented, and the semi-global asymptotic convergence was achieved via rigorous theoretical analysis and Lyapunov proof. The results of simulations demonstrated the superiority and robustness of this approach in resolving the trajectory tracking challenges of DP ships. Moreover, the proposed control scheme is highly effective in optimizing the trajectory tracking of DP ships, as it takes into account the multiple constraints that may arise during the process, which is a valuable contribution to the field of DP ship trajectory tracking, providing a more comprehensive and effective solution to this complex problem.

Design of UDE-based finite-time fault-tolerant control for DP vessels with complex disturbances and input constraints

This paper presents a novel uncertainty and disturbance estimator (UDE) – based finite-time fault-tolerant control scheme, applied to the controller design of dynamic positioning (DP) vessels with environmental disturbances, model uncertainty, input saturation, and thruster failures. The proposed method employs a non-singular terminal sliding surface to achieve finite-time stability based on the vessel's position and velocity errors. A UDE is to estimate environmental disturbances and model uncertainties. The auxiliary dynamic system (ADS) and smooth switching function are adopted to compensate for input constraints and avoid singularity caused by ADS. Further, a proposed fault observer is to estimate the thruster's fault state. In partial thruster failure, the utilization of the remaining execution ability of the failed thrusters to achieve vessel control objectives. Theoretical and simulation results show that the proposed method has a fast response, high control accuracy, strong disturbance resistance, and fault tolerance, making it superior to recent and classical trajectory tracking control.

Development of a hybrid simulation and experiment test platform for dynamic positioning vessels

ABSTRACT The harsh ocean environment and complex operating conditions require high dynamic positioning (DP) capability of offshore vessel. The design, development, and performance evaluation of DP systems are generally carried out through numerical simulations or scale-model experiments. Simulations are a convenient and inexpensive alternative to experiments, but may not be practical due to the simplicity of the model. Thus, this paper presents a hybrid simulation and experiment test platform for DP vessels. For realistic calculating environmental loads and kinematic responses in hydrodynamic simulations, the 6DoF model is used to describe the dynamic response of DP vessels, considering fluid-memory effects and frequency-dependent hydrodynamic parameters. In the experiments, similarity theory based on the same Froude number is used to ensure consistency of the control parameters with the simulations, reducing the time for experimental tuning. Finally, a case study of a DP shuttle tanker is used to verify the credibility of the test platform.

A modified predictive PID controller for dynamic positioning of vessels with autoregressive model

This paper proposes a modified predictive PID controller for dynamic positioning (DP) vessels. The combination of PID and motion prediction techniques remains the simple structure and improves the performance of DP system in station keeping. The phase of the proportional force produced by controller is advanced to compensate the slowly-varying force. Several short-term motion prediction techniques are compared and the autoregressive model is the most appropriate one for the proposed control scheme. The influence of the prediction length and environmental condition are investigated. Simulation results of proposed controller are compared with those of the PID controller and the PID with acceleration feedback (AFB). The comparisons suggest that the proposed controller is an attractive alternative to PID with AFB when the acceleration cannot be accurately measured.

Discrete Wavelet Transform-based Approach of Real-time Wave Filtering for Dynamic Positioning of Marine Vessels

The ongoing development of deep-sea resources has contributed toward the widespread use of dynamic positioning (DP) systems that can operate in arbitrary sea areas without the limitation of operating water depth compared to mooring systems. The second-order slowly varying forces can be compensated by DP feedback control. Wave filtering techniques are crucial for designing the DP controller, which can improve the positioning performance and service of multiple actuators. The objective of wave filtering is to separate the motion induced by the first-order high-frequency oscillatory waves from the motion caused by the second-order low-frequency (LF) slowly varying disturbances. This paper proposes a novel real-time wave filtering approach for DP systems on the basis of the discrete wavelet transform without relying on the previous knowledge of dynamics of the vessel. Meanwhile, to suppress the end effects of wavelet filtering and achieve real-time application, symmetric boundary extension and the sliding window method are investigated in order to capture the slow time-varying LF motion of DP vessels. The proposed real-time wavelet filtering approach can significantly improve the quality of the separated components by selecting the optimal decomposition level, wavelet function parameters, and threshold denoising method. The real-time and filtering performance of the proposed approach is verified using a scaled model of an offshore supply vessel as well as full-scale experiments on the EXPLOERE I scientific investigation vessel under the action of winds, currents, and waves. The experimental results confirm that the proposed wave filtering algorithm is efficient and that the LF component waveform can be effectively recovered after wavelet filtering.

Modelling of the Dynamically Positioned vessels and managed ice-field interaction dynamics using empirical-statistical techniques

Empirical models based on measurements are often preferred over high-fidelity numerical models when real-time predictions of broken ice-field actions on ships and floating offshore platforms are needed. This paper presents the methodologies and validations of a novel numerical model based on empirical-statistical techniques for predicting dynamically positioned (DP) vessels and ice-field interactions. The authors developed multiple regression models for predicting the time-averaged and average peaks of thruster forces and yawing moment of the DP vessel due to managed ice actions. Monte Carlo Simulations and other empirical techniques were then developed to predict the time series of the ice forces and moment on the DP vessel in managed ice scenarios. The authors then carried out model validations for several ice-field and vessel operating scenarios. The model showed reasonable accuracy in predicting the effects of several ice-field parameters on the forces and moments of two DP-controlled vessels. Subsequently, integration of the model into a DP-in-ice validation platform is offered, which can simulate and optimize the performance of a DP-controlled vessel in ice-infested water. Finally, the uniqueness and limitations of the models are discussed, and recommendations for future work are provided.

Event-triggered neuroadaptive predefined practical finite-time control for dynamic positioning vessels: A time-based generator approach

This paper discusses the predefined practical finite-time (PPFT) dynamic positioning (DP) control problem for DP vessels subject to internal/external uncertainties. Those heterogeneity uncertainties are handled by a separate-type treatment approach. The finite-time (FT) DP control is fulfilled by a predefined FT function on the basis of a time-based generator (TBG). Under the dynamic surface control together with the TBG design framework, the convergence time and control accuracy of the DP system can be determined by the designer offline. Meanwhile, the virtual derivation and computational burden problems are dissolved by using a first-order filter and virtual parameter learning technique. To reduce mechanical wear, an event-triggering protocol between the control law and the actuator is built to reduce the operating frequency of the actuator. An event-triggered neuroadaptive PPFT control scheme is presented for DP vessels. The stability of the closed-loop DP control systems is validated via the Lyapunov theorem. Approach efficiency is confirmed by numerical examples.

Assessment of Station Keeping Capability for Dynamically Positioned Offshore Supply Vessel

Abstract Dynamic positioning capability (DPCap) assessment is essential during the preliminary investigation of the positioning ability of a newly designed dynamic positioning system (DPS). Operational safety is always the first consideration in the design and operation of a new DPS. To be able to plan a safe and efficient operation, it is important to know the maximum environmental conditions that the DP vessel can withstand. The main objective of this article is to present a developed tool capable of conducting the DPCap analysis for offshore supply vessels based on the DNVGL methodology. The article describes the background studies, methodology, and mechanism associated with the formulation and development of the tool. The optimization of the design variables for the station-keeping criteria targets the highest capability at the lowest possible energy expenditure. Finally, the results are presented in the form of standard capability polar plots to show the application of the computational assessment procedure. The accuracy and reliability of the DPCap tool are validated by comparing with the results of the commercial one for the same case study ship at multiple simulation cases. Introduction Many offshore operations require the vessel to be kept in a position to face environmental loads from wind, waves and current. This can be achieved using Dynamic Positioning System (DPS) by using thrusters (Sørensen 2011). Station keeping ability is used in different types of vessels with varying operational requirements. Cruise vessels and mega yachts use it in the form of virtual anchoring to avoid the environmental impact of anchoring in the sensitive estuaries they often ply. The importance of DPS is growing because of the increase in the number of offshore construction sites requiring DPS for their operations (Mehrzadi et al. 2020). Over the last four decades, dynamic positioning (DP) in deep water has been used in the marine sector, oil and gas industries, and military services in many tasks such as drilling, oil and gas floating production platform, cable, and pipe laying, docking and towing, firefighting, supply, search and rescue, surveying, and mobile offshore bases (MOB). Each of these operations benefits from a DP system’s ability to include precise position-keeping, freedom from the restrictions of mooring spread systems, the ability to move quickly from one site to another, and track-keeping capabilities (Elkafas et al. 2021; Mahfouz 2007).

Cascade-based tracking control for dynamic positioning vessels under unknown sea loads

This paper investigates the trajectory tracking control problem of a dynamic positioning vessel (DPV) subject to tracking performance, time-varying sea loads and inertia uncertainties. Firstly, a novel cascade model of the DPV is built to describe the dynamics. Then, in order to guarantee the performance constraints of the trajectory tracking, a new-type fixed-time performance function is proposed, which can not only guarantee the transient and steady-state performance during task execution, but also achieve convergence at a fixed time. In addition, an adaptive neural network is introduced to handle the problem of parameter uncertainty and unknown sea loads in the system. Furthermore, it is proved by the Lyapunov stability theory that all signals in the closed-loop system are bounded . Finally, numerical simulation results verified the effectiveness of the proposed method and ensure that the tracking error is stable in the interval of 2 m or less within a given time.

Computation of environmental loads towards an accurate dynamic positioning capability analysis

Dynamic positioning (DP) systems enable vessels keep station or maintain a predetermined track under specified environmental and operational conditions. This ability is required for many offshore operations such as drilling, supplying, heavy lifting, pipe laying and diving support. In order to establish the maximum environmental conditions in which a DP vessel can maintain its position and heading for a proposed thruster configuration, a DP capability analysis (DPCAP) must be carried out. The most crucial step in a DPCAP is to estimate the environmental loads due to the wind, current and waves in an accurate, consistent and efficient manner. In this paper semi-empirical methods and a RANS based analysis tool compared in order to determine the reliability of such approaches for the purpose of early assessment of wind, wave and current loads. The main focus of this approach is to cross validate the semi-empirical and CFD based procedures so that the initial rough estimates on the loads could be refined accurately as well as to provide a comprehensive example of a reference work for the literature. Typical DP capability polar plots for an offshore supply vessel are presented to illustrate the application of the computational analysis procedure.

Sliding mode control unified with the uncertainty and disturbance estimator for dynamically positioned vessels subjected to uncertainties and unknown disturbances

When considering the characteristics of a dynamic positioning (DP) vessel involving uncertain modelling and unknown disturbances, the vessel control problem becomes a mismatched nonlinear control issue. To resolve this issue, this paper proposes a control method based on the sliding mode control (SMC) and the uncertainty and disturbance estimator (UDE). The total disturbance in the DP vessel is determined using the UDE, whereas the trajectory tracking function of the vessel is achieved via SMC. Moreover, the estimation error in the UDE and the tracking error of the SMC were found to converge based on the Lyapunov theory. The effectiveness of the proposed control strategy was verified via simulations. A comparative study involving the conventional SMC was also conducted, revealing that the proposed SMC unified with the UDE yielded a considerably superior performance.

Event-Based Prescribed Performance Control for Dynamic Positioning Vessels

This brief investigates the problem of trajectory tracking control for dynamic positioning vessels under unknown and time-varying sea loads. A novel event-based prescribed performance control (PPC) scheme is proposed to address this problem. Firstly, an integral disturbance observer (IDO) is introduced to estimate the sea loads, and a new-type performance function (PF), which has advantages of flexible shape and appointed-time convergence, is proposed to form the funnel envelops for the trajectory tracking errors. Subsequently, by employing the IDO and the new-type PF, a dynamic event-triggered control policy is designed in the PPC framework to achieve the assigned trajectory tracking mission in an appointed-time with guaranteed performance bounds and less operating frequency of the actuator. The salient feature of the derived controller is that it can achieve a trade-off between the tracking performance and actuators’ operating frequency in a well-balanced way, which is of great importance for practical engineering applications. Finally, simulation results are given to show the effectiveness of the proposed method.

TWINBOT: Autonomous Underwater Cooperative Transportation

Underwater Inspection, Maintenance, and Repair operations are nowadays performed using Remotely Operated Vehicles (ROV) deployed from dynamic-positioning vessels, having high daily operational costs. During the last twenty years, the research community has been making an effort to design new Intervention Autonomous Underwater Vehicles (I-AUV), which could, in the near future, replace the ROVs, significantly decreasing these costs. Until now, the experimental work using I-AUVs has been limited to a few single-vehicle interventions, including object search and recovery, valve turning, and hot stab operations. More complex scenarios usually require the cooperation of multiple agents, i.e., the transportation of large and heavy objects. Moreover, using small, autonomous vehicles requires consideration of their limited load capacity and limited manipulation force/torque capabilities. Following the idea of multi-agent systems, in this paper we propose a possible solution: using a group of cooperating I-AUVs, thus sharing the load and optimizing the stress exerted on the manipulators. Specifically, we tackle the problem of transporting a long pipe. The presented ideas are based on a decentralized Task-Priority kinematic control algorithm adapted for the highly limited communication bandwidth available underwater. The aforementioned pipe is transported following a sequence of poses. A path-following algorithm computes the desired velocities for the robots' end-effectors, and the on-board controllers ensure tracking of these setpoints, taking into account the geometry of the pipe and the vehicles' limitations. The utilized algorithms and their practical implementation are discussed in detail and validated through extensive simulations and experimental trials performed in a test tank using two 8 DOF I-AUVs.

Design of UDE-based dynamic surface control for dynamic positioning of vessels with complex disturbances and input constraints

In practice, dynamic positioning (DP) vessels are subjected to complex disturbances as well as the magnitude and changing rate constraints of the thrusts and moments. This study applied a dynamic surface controller based on an uncertainty and disturbance estimator (UDE) to a DP vessel with complex disturbances and input constraints. The UDE was designed to estimate and handle the complex disturbances. An auxiliary dynamic system (ADS) and smooth switching function were employed to compensate for the input constraints and avoid the singularity phenomenon caused by the ADS, respectively. The combination of the UDE method and dynamic surface control (DSC) technology significantly simplified the design process for the control law and increased the practicability for DP vessels. The stability of the proposed control law was proved using the Lyapunov theory. The effectiveness of the control law and possibility of actually applying it to a DP vessel were verified using simulation experiments.

A Novel Densely Connected Convolutional Neural Network for Sea-State Estimation Using Ship Motion Data

The sea-state estimation is a fundamental problem in the development of autonomous ships. Traditional methods such as wave buoy, satellites, and wave radars are limited by locations, clouds, and costs, respectively. Model-based methods are prone to incorrect estimations due to their high dependence on mathematical models of ships. As previous data-driven studies for sea-state estimation consider only wave height and use the motion data from dynamic positioning (DP) vessels, this article introduces a new, deep neural network (SSENET) to estimate sea state in light of both wave height and wave direction and extends the generality of sensor data from ship motion with forward speed. SSENET is built on the basis of stacked convolutional neural network (CNN) blocks with dense connections between different blocks, channel attention modules, and a feature attention module. The dense connections build short-cut paths between input and all subsequent convolutional blocks, which can make full use of all the hierarchical features from the original time-series sensor data. The channel attention modules aim to enhance the features extracted by each convolution block. The feature attention module focuses on combining the feature fusion of hierarchical features in an adaptive manner. Benchmark experiments show the competitive performance against the state-of-the-art approaches. Applying the SSENET on two data sets of a zigzag motion for comparative studies shows the effectiveness of the proposed method.

A Deep Learning Method for Short-Term Dynamic Positioning Load Forecasting in Maritime Microgrids

The dynamic positioning (DP) system is a progressive technology, which is used in marine vessels and maritime structures. To keep the ship position from displacement in operation mode, its thrusters are used automatically to control and stabilize the position and heading of vessels. Hence, the DP load forecasting is already an essential part of DP vessels, which the DP power demand from the power management system (PMS) for thrusting depends on weather conditions. Furthermore, the PMS is used to control power generation, and prevent power failure, limitation. To perform station keeping of vessels by DPS in environmental changes such as wind, waves, capacity, and reliability of the power generators. Hence, a lack of power may lead to lower DP performance, loss of power, and position, which is called shutdown. Therefore, precise DP power demand prediction for maintaining the vessel position can provide the PMS with sufficient information for better performance in a complex decision-making process for the DP vessel. In this paper, the concept of deep learning techniques is introduced into DPS for DP load forecasting. A Levenberg–Marquardt algorithm based on a nonlinear recurrent neural network is employed in this paper for predicting thrusters’ power consumption in sea state variations due to challenges in power generation with the relative degree of accuracy by combining weather parameter dependencies as environmental disturbances. The proposed method evaluates with three traditional forecasting methods through a set of practical real-time DP load and weather parametric data. Numerical analysis has shown that with the proposed method, the future DP load behavior can be predicted more accurately than that obtained from the traditional methods, which greatly assists in operation and planning of power system to maintain system stability, security, reliability, and economics.

Recursive-Biased-Generalized-Inverse Method for Thrust Allocation for Dynamic Positioning Vessels with Non-Rotatable Thrusters and Force Constraints

This paper presents a novel method for thrust allocation for DP vessels with non-rotatable thrusters and force constraints. It uses a modified cost function of the thrust allocation problem for non-rotatable thruster and without force constraints, such that the force constraint can be handled. The proposed method runs in a recursive way to minimize the allocation error. The proposed method is different from the known methods in the literature. The completeness and continuity of the proposed algorithm are studied to verify the effectiveness of the proposed method.

On the Usage of Low-Cost MEMS Sensors, Strapdown Inertial Navigation, and Nonlinear Estimation Techniques in Dynamic Positioning

In this article, we suggest that a strapdown inertial navigation system based on microelectromechanical system (MEMS) inertial sensors is a useful addition to a vessel with dynamic positioning (DP). We conduct full-scale experiments with MEMS inertial sensors on board a DP vessel operating off the Norwegian coast. The vessel operates in different scenarios, and the purpose is to showcase how low-cost MEMS sensors may complement or replace existing DP sensor systems. Employing nonlinear observers for estimating attitude, heave, velocity, and position, we go through the benefits and disadvantages, and some caveats, for the sensors and methods used in this article. Two different MEMS units are evaluated, aided by gyrocompasses and position reference systems. We evaluate the attitude, heave, and dead reckoning capabilities obtained with the presented estimators, in relation to relevant class notation, ultimately motivating the inclusion of new sensors and methods for dynamic positioning. The results related to attitude and heave are compared with data from well-proven industry standard vertical reference units while dead reckoning is evaluated with respect to the onboard position reference systems.