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

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

Design of hybrid controller for dynamic positioning from calm to extreme sea conditions

10.1016/j.automatica.2006.11.017Automatica435768-785ATCA

Application of Machine and Deep Learning to Mooring, Dynamic Positioning, and Ship Berthing Systems

In recent years, there have been a surge of advances in machine and deep learning due to accessibility to a large amount of digital data, developments in computer hardware, and state-of-the-art machine and deep learning algorithms proposed. The robust performance of the recent machine and deep learning algorithms have been proven in many applications such as natural language processing, computer vision, market research, self-driving car, autonomous shipping, and so on. The application of machine and deep learning is very powerful in a sense that one does not need to build such a complex and hard-coded system to implement sophisticated functionality. Instead, a machine and deep learning-based system can be trained on a collected training dataset and the trained system can robustly perform as desired. There are two main advantages of the use of machine and deep learning-based systems over the traditional hard-coded systems. First, as mentioned, the machine and deep learning-based systems do not require such complex and hard-coded algorithms, therefore, such learning systems are less prone to errors and faster to implement without much debugging. Second, the machine and deep learning-based systems can adapt to varying circumstances through re-training based on collected data. An example of the varying circumstance can be a varying purchase trend impacted by the media. Therefore, even if the input distribution from the circumstance changes over time, the machine and deep learning-based systems can easily adapt. In this paper, the machine and deep learning algorithms are applied to various applications such as a mooring system, dynamic positioning system (DPS), and ship berthing system. Specifically, the machine and deep learning algorithms are utilized to build a mooring line tension prediction system, a feed-forward system for DPS, an adaptive proportional-integral-derivative (PID) controller for DPS, and an automatic ship berthing system.1. Introduction 1 2. Background of Machine and Deep Learning 4 2.1 Machine Learning 4 2.2 Deep Learning 9 2.2.1 Types of Deep Learning Layers 9 2.2.2 Activation Function and Weight Initialization Methods 18 2.2.3 Optimizers 19 2.2.4 Training Dataset Scaling 26 2.2.5 Transfer Learning 28 2.3 Reinforcement Learning 28 3. Machine Learning-Based Mooring Line Tension Prediction System 39 3.1 Introduction 39 3.2 Brief Comparison Between Conventional and Proposed Mooring Line Tension Prediction Systems 40 3.3 Proposed K-Means-Based Sea State Selection Method 41 3.3.1 Padding 42 3.3.2 K-Means 44 3.3.3 K-Means-Based Monte Carlo Method 45 3.3.4 Feature Vector Generation 47 3.3.5 Clustering of Relevant Sampled Sea States with K-Means 48 3.4 Proposed Hybrid Neural Network Architecture 50 3.4.1 Architecture 50 3.4.2 Training Procedure 54 3.5 Simulation and Result Discussion 55 3.5.1 Simulation Conditions 55 3.5.2 Overall Hs-focused NN model 56 3.5.3 Effectiveness of Batch Normalization 59 3.5.4 Low Hs-focused NN model 60 3.5.5 Proposed Hybrid Neural Network Architecture 61 4. Motion Predictive Control for DPS Using Predicted Drifted Ship Position Based on Deep Learning and Replay Buffer 65 4.1 Introduction 65 4.2 PID Feed-Back System and Wind Feed-Forward System 66 4.3 Proposed Motion Predictive Control 69 4.4 Numerical Modeling of Target Ship's Behavior 73 4.4.1 Target Ship and DPS 73 4.4.2 Equation of Motion of Target Ship 74 4.5 Effectiveness of Proposed Algorithms 76 4.5.1 Simulation Conditions 76 4.5.2 Types of Deep Learning Layers 77 4.5.3 Real-Time Normalization Method 78 4.5.4 Replay Buffer 80 4.6 Simulation and Result Discussion 81 4.6.1 Simulation Under One Environmental Condition 81 4.6.2 Simulation Under Two Different Sequential Environmental Conditions 84 5. Reinforcement Learning-Based Adaptive PID Controller for DPS 88 5.1 Introduction 88 5.2 Target Ship and DPS 90 5.2.1 PID Control in DPS 91 5.2.2 Hydrodynamics Associated with a Drifting Motion of a Ship 93 5.3 Proposed Adaptive Fine-Tuning System for PID Gains in DPS 95 5.4 Simulation Results 99 5.4.1 Effectiveness of the Proposed Adaptive Fine-Tuning System 99 5.4.2 Overall Performance Assessment 103 5.5 Discussion 107 6. Application of Recent Developments in Deep Learning To ANN-based Automatic Berthing System 111 6.1 Introduction 111 6.2 Mathematical Model of Ship Maneuvering 112 6.2.1 Mathematical Model for Ship-Maneuvering Problem 113 6.2.2 Modeling of Propeller and Rudder 114 6.3 Artificial Neural Network and Important Factors in Training the Network 115 6.3.1 Artificial Neural Network 115 6.3.2 Optimizer 117 6.3.3 Input Data Scaling 117 6.3.4 Number of Hidden Layers 118 6.3.5 Overfitting Prevention 118 6.4 Application of Recent Developments in Deep Learning to Automatic Berthing 119 6.5 Simulation and Result Discussion 125 7. Conclusion 131 7.1 Machine Learning-Based Mooring Line Tension Prediction System 131 7.2 Motion Predictive Control for DPS Using Predicted Drifted Ship Position Based on Deep Learning and Replay Buffer 132 7.3 Reinforcement Learning-Based Adaptive PID Controller for DPS 133 7.4 Application of Recent Developments in Deep Learning to ANN-Based Automatic Berthing System 134Maste

INTELLIGENT ESTIMATION IN DYNAMIC POSITIONING SYSTEMS OF MARINE VESSELS

Sustavi za dinamičko pozicioniranje plovnih objekata koriste se za održavanje njihove pozicije, smjera napredovanja i brzine, održavanje unaprijed definirane putanje gibanja, potpomognuto sidrenje i sl. Da bi se ove operacije uopće mogle provoditi, nužno je, između ostalog, omogućiti i određivanje precizne estimacije niskofrekventne pozicije, smjera napredovanja i brzine plovnog objekta, te estimaciju vjetrovnog i sporopromjenjivog opterećenja koje uzrokuju ostali vanjski poremećaji. U realnim sustavima za pozicioniranje plovnih objekata funkciju observera, tj. estimatora, ima neka od inačica Kalmanovog filtra koji ima već dugu tradiciju u brodskim sustavima upravljanja. U radu su analizirani klasični koncepti na kojima su temeljeni postojeći sustavi za dinamičko pozicioniranje te su istražene značajke dinamičkog pozicioniranja plovnih objekata s teoretske i praktične strane, posebno u dijelu koji se odnosi na problematiku filtriranja, identifikacije, estimacije i predikcije. Uočene su brojne prednosti, ali i nedostaci postojećih rješenja koji se mogu otkloniti primjenom novijih računalnih tehnologija kao što su algoritmi strojnog učenja i računalne inteligencije. Iz navedenih razloga, predložene su i konstruirane strukture statičkih, dinamičkih i hibridnih inteligentnih identifikatora i estimatora za potrebe identifikacije i estimacije u sustavima za dinamičko pozicioniranje. Od posebnog značaja su predloženi hibridni sustavi inteligentnih identifikatora i estimatora s proširenim Kalmanovim filtrom te inteligentni identifikatori za fuziju senzorskih informacija i rekonstrukciju signala u prekidu. Predloženi inteligentni identifikatori i estimatori su verificirani na realnim mjerenjima DP Log arhive dizaličara i cjevopolagača Saipem 7000 tijekom postupka polaganja cijevi na Projektu Ormen Lange (Norveška, 2006.).Dynamic positioning (DP) systems are used for maintaining position, heading and speed of the vessels, but also a predefined motion path, position mooring, etc. To ensure performing of these operations, it is necessary, among other things, to determine an accurate estimation of low-frequency position, heading and speed of the vessel. Additionally, it is necessary to ensure the estimation of wind and slowly-varying loads caused by other environmental disturbances. In actual DP systems, the vessel observer is usually an extended Kalman filter (EKF) which is traditionally used in marine control systems. In this doctoral thesis the classical base concepts of the existing commercial DP systems are analysed. Furthermore, the characteristics of DP systems are analysed both from the theoretical and practical point of view, especially in the part which is closely related to filtering, identification, estimation and prediction. Numerous advantages of existing solutions are identified, but also the several disadvantages which can be eliminated by using modern computational technologies such as machine learning and computational intelligence algorithms are pointed out. For these reasons, structures based on static, dynamic and hybrid intelligent identifiers and estimators have been proposed for the purpose of intelligent identification and estimation in DP systems. Proposed hybrid system of intelligent identifiers and estimators combined with EKF, as well as the intelligent identifiers for the sensor fusion and reconstruction of lost signals, are of particular interest. Intelligent identifiers and estimators are further adjusted, tested, and verified with real measurements from the DP Log archive of the heavy-lift and J-lay pipe vessel Saipem 7000

INTELLIGENT ESTIMATION IN DYNAMIC POSITIONING SYSTEMS OF MARINE VESSELS

Sustavi za dinamičko pozicioniranje plovnih objekata koriste se za održavanje njihove pozicije, smjera napredovanja i brzine, održavanje unaprijed definirane putanje gibanja, potpomognuto sidrenje i sl. Da bi se ove operacije uopće mogle provoditi, nužno je, između ostalog, omogućiti i određivanje precizne estimacije niskofrekventne pozicije, smjera napredovanja i brzine plovnog objekta, te estimaciju vjetrovnog i sporopromjenjivog opterećenja koje uzrokuju ostali vanjski poremećaji. U realnim sustavima za pozicioniranje plovnih objekata funkciju observera, tj. estimatora, ima neka od inačica Kalmanovog filtra koji ima već dugu tradiciju u brodskim sustavima upravljanja. U radu su analizirani klasični koncepti na kojima su temeljeni postojeći sustavi za dinamičko pozicioniranje te su istražene značajke dinamičkog pozicioniranja plovnih objekata s teoretske i praktične strane, posebno u dijelu koji se odnosi na problematiku filtriranja, identifikacije, estimacije i predikcije. Uočene su brojne prednosti, ali i nedostaci postojećih rješenja koji se mogu otkloniti primjenom novijih računalnih tehnologija kao što su algoritmi strojnog učenja i računalne inteligencije. Iz navedenih razloga, predložene su i konstruirane strukture statičkih, dinamičkih i hibridnih inteligentnih identifikatora i estimatora za potrebe identifikacije i estimacije u sustavima za dinamičko pozicioniranje. Od posebnog značaja su predloženi hibridni sustavi inteligentnih identifikatora i estimatora s proširenim Kalmanovim filtrom te inteligentni identifikatori za fuziju senzorskih informacija i rekonstrukciju signala u prekidu. Predloženi inteligentni identifikatori i estimatori su verificirani na realnim mjerenjima DP Log arhive dizaličara i cjevopolagača Saipem 7000 tijekom postupka polaganja cijevi na Projektu Ormen Lange (Norveška, 2006.).Dynamic positioning (DP) systems are used for maintaining position, heading and speed of the vessels, but also a predefined motion path, position mooring, etc. To ensure performing of these operations, it is necessary, among other things, to determine an accurate estimation of low-frequency position, heading and speed of the vessel. Additionally, it is necessary to ensure the estimation of wind and slowly-varying loads caused by other environmental disturbances. In actual DP systems, the vessel observer is usually an extended Kalman filter (EKF) which is traditionally used in marine control systems. In this doctoral thesis the classical base concepts of the existing commercial DP systems are analysed. Furthermore, the characteristics of DP systems are analysed both from the theoretical and practical point of view, especially in the part which is closely related to filtering, identification, estimation and prediction. Numerous advantages of existing solutions are identified, but also the several disadvantages which can be eliminated by using modern computational technologies such as machine learning and computational intelligence algorithms are pointed out. For these reasons, structures based on static, dynamic and hybrid intelligent identifiers and estimators have been proposed for the purpose of intelligent identification and estimation in DP systems. Proposed hybrid system of intelligent identifiers and estimators combined with EKF, as well as the intelligent identifiers for the sensor fusion and reconstruction of lost signals, are of particular interest. Intelligent identifiers and estimators are further adjusted, tested, and verified with real measurements from the DP Log archive of the heavy-lift and J-lay pipe vessel Saipem 7000

INTELLIGENT ESTIMATION IN DYNAMIC POSITIONING SYSTEMS OF MARINE VESSELS

Sustavi za dinamičko pozicioniranje plovnih objekata koriste se za održavanje njihove pozicije, smjera napredovanja i brzine, održavanje unaprijed definirane putanje gibanja, potpomognuto sidrenje i sl. Da bi se ove operacije uopće mogle provoditi, nužno je, između ostalog, omogućiti i određivanje precizne estimacije niskofrekventne pozicije, smjera napredovanja i brzine plovnog objekta, te estimaciju vjetrovnog i sporopromjenjivog opterećenja koje uzrokuju ostali vanjski poremećaji. U realnim sustavima za pozicioniranje plovnih objekata funkciju observera, tj. estimatora, ima neka od inačica Kalmanovog filtra koji ima već dugu tradiciju u brodskim sustavima upravljanja. U radu su analizirani klasični koncepti na kojima su temeljeni postojeći sustavi za dinamičko pozicioniranje te su istražene značajke dinamičkog pozicioniranja plovnih objekata s teoretske i praktične strane, posebno u dijelu koji se odnosi na problematiku filtriranja, identifikacije, estimacije i predikcije. Uočene su brojne prednosti, ali i nedostaci postojećih rješenja koji se mogu otkloniti primjenom novijih računalnih tehnologija kao što su algoritmi strojnog učenja i računalne inteligencije. Iz navedenih razloga, predložene su i konstruirane strukture statičkih, dinamičkih i hibridnih inteligentnih identifikatora i estimatora za potrebe identifikacije i estimacije u sustavima za dinamičko pozicioniranje. Od posebnog značaja su predloženi hibridni sustavi inteligentnih identifikatora i estimatora s proširenim Kalmanovim filtrom te inteligentni identifikatori za fuziju senzorskih informacija i rekonstrukciju signala u prekidu. Predloženi inteligentni identifikatori i estimatori su verificirani na realnim mjerenjima DP Log arhive dizaličara i cjevopolagača Saipem 7000 tijekom postupka polaganja cijevi na Projektu Ormen Lange (Norveška, 2006.).Dynamic positioning (DP) systems are used for maintaining position, heading and speed of the vessels, but also a predefined motion path, position mooring, etc. To ensure performing of these operations, it is necessary, among other things, to determine an accurate estimation of low-frequency position, heading and speed of the vessel. Additionally, it is necessary to ensure the estimation of wind and slowly-varying loads caused by other environmental disturbances. In actual DP systems, the vessel observer is usually an extended Kalman filter (EKF) which is traditionally used in marine control systems. In this doctoral thesis the classical base concepts of the existing commercial DP systems are analysed. Furthermore, the characteristics of DP systems are analysed both from the theoretical and practical point of view, especially in the part which is closely related to filtering, identification, estimation and prediction. Numerous advantages of existing solutions are identified, but also the several disadvantages which can be eliminated by using modern computational technologies such as machine learning and computational intelligence algorithms are pointed out. For these reasons, structures based on static, dynamic and hybrid intelligent identifiers and estimators have been proposed for the purpose of intelligent identification and estimation in DP systems. Proposed hybrid system of intelligent identifiers and estimators combined with EKF, as well as the intelligent identifiers for the sensor fusion and reconstruction of lost signals, are of particular interest. Intelligent identifiers and estimators are further adjusted, tested, and verified with real measurements from the DP Log archive of the heavy-lift and J-lay pipe vessel Saipem 7000

Automatic marine: a review from a control point of view.

[EN] Automatic control is an horizontal subject and many of their branches are applied in the marine fields: robotics, control engineering, artificial intelligence, modeling and simulation, sensors and actuators. The paper presents an overview of some of the major advances that have taken place from the point of view of marine vehicles modeling, identification and control.[ES] La Automática es una disciplina horizontal muchos de cuyos temas se aplican en el campo del sector marítimo, como son: la robótica, la ingeniería de control, la inteligencia artificial, el modelado y la simulación, los sensores y los actuadores. En este trabajo hacemos una revisión de los avances que han tenido lugar en los últimos años desde el punto de vista del modelado, la identificación y el control de los vehículos marinos.Este trabajo ha sido desarrollado gracias al apoyo de la Secretaría de Estado de Investigación, Desarrollo e Innovación mediante los proyectos coordinados DPI2009-14552-C02-01 y DPI2009-14552-C02-02.De La Cruz García, JM.; Aranda Almansa, J.; Girón Sierra, JM. (2012). Automática marina: una revisión desde el punto de vista del control. Revista Iberoamericana de Automática e Informática industrial. 9(3):205-218. https://doi.org/10.1016/j.riai.2012.05.001OJS20521893ABS, 2006. Guide for Vessel Maneuverability. American Bureau of Shipping. ABS Plaza 16855 Northchase Drive, Houston, TX 77060 USA.Aguiar et al., Aguiar, A.P., Hespanha, J.P. and Kokotović, P. 2005.Path-following for non- minimum phase systems removes performance limitations. IEEE Trans. Autom. 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