Generalized Predictive Control of Ship Coupling Motions Using Active Flume Tanks

This dissertation uses the Generalized Predictive Control (GPC) approach to design a control system for a ship rolling motion coupled with the sway and yaw using an activated flume tank. GPC is a strategy based on system output prediction over finite horizon known as the prediction horizon. GPC controller is designed from the coefficients of the Autoregressive model with exogenous input (ARX) that are computed directly from input and output data. It computes the future control input based on the cost function with weighted input and output. System identification approach is implemented on the system to find the ARX coefficients parameters. A mathematical model of the anti-rolling flume tank and the ship coupling model are derived to be in the state space form. The time domain model of the ship motions has been extended to predict the coupling motions of sway, yaw and roll. Also, the disturbance model is generated as irregular waves. Analyses for the ship rolling and coupling models, with and without the anti-rolling flume tank, are presented. A numerical simulation using the MATLAB program is implemented. The numerical simulation indicates that there are three factors that affect the ship motions: sea state conditions, wave attack angle and ship control system. The simulation result shows that the passive control system using an anti-rolling flume tank is able to reduce the ship rolling angle up to fifty percent. In comparison, simulation result of the actively controlled system using GPC shows that the ship rolling angle can be mitigated up to eighty percents. The GPC approach is tested on the ship model in different weather conditions. The numerical simulation is implemented to evaluate the controller performance and investigate the benefit of the GPC in the ship coupling motions. The numerical results show that the coupling model of roll, sway and yaw can affect each other simultaneously. The roll motion can be affected by the sway force more than yaw moment. The effect and performance of the GPC in controlling the ship roll motion in different wave\u27s disturbances and sea state conditions are discussed

Passive and Active Nonlinear Control of Ship Roll Motions Using U-Tube Tanks

A U-tube water tank is first designed to roll a ship floating on still water. The 6-degree of freedom (DOF) dynamic model of U-tube tank is derived and the effects of its parameters on ship roll motion are studied. Numerical simulations show that the U-tube tank is an effective stimulator to roll the ship on still water. For a rolling ship, the U-tube tank can be used as a damper to reduce ship roll motion quickly. Active control of ship roll motion with a proportional and derivative (PD) controller, linear quadratic regulator (LQR), generalized predictive control (GPC), and deadbeat predictive control (DPC) is studied using a U-tube water tank as actuator is studied. For the predictive control, system identification is applied to update the parameters of the linear ship roll model with a U-tube tank when the ship dynamics changes. Numerical simulations show that GPC has the best performance and the U-tube tank is effective in ship roll mitigation. Nonlinear ship roll mitigation with passive U-tube tank, U-tube tank using feedback linearization with completely known system parameters, and U-tube tank using adaptive fuzzy feedback linearization control with unknown system parameters, are also studied. In numerical simulation, a passive U-tube tank and feedback linearization help to reduce ship roll motion and capsizing compared to a ship without the U-tube tank. Feedback linearization is the most effective means of controlling ship roll motion, and adaptive feedback linearization is more effective than a passive U-tube tank

Optimal Feedback Control for Ship Roll Motion Under Sea States

The primary influences of ship motion are roll motion. The purpose of this dissertation is to discuss a means to reduce the roll amplitude of ship motion in the case of zero forward speed using the roll mitigation device known as the flume tank, or U-tube, tank. Passive control and active control are studied. Optimal feedback control is the designated algorithm for activated roll mitigation device. The assumed model in this study is a submarine chaser. The linear coupled equation of swaying, rolling, and yawing motion of this assumed model is studied. The roll motion of this vessel is investigated under Sea State 3. The irregular wave, which attacked the ship hull, is studied with different encounter angles. The large roll amplitude is found when the wave encounters ship hull at Beam Sea (β = 90°). The simulation results of passive control demonstrate that the flume tank creates high damping system under various wave angle\u27s attacks. This mitigation device can cancel the roll amplitudes over 50%. For an activated anti-rolling tank, the optimal feedback control (LQR) shows that the full-state-variables method results in a high-damped system as well as the suboptimal feedback control. Technically, the linear coupled equation of motion should improve to the nonlinear range in order to obtain higher accuracy of the ship\u27s motion and online control algorithms should be developed

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

SWING-UP CONTROL OF MASS BODY INTERLINKED FLEXIBLE TETHER

One of the applications of tether system is in the field of satellite technology, where the mother ship and satellite equipment are connected with a cable. In order to grasp the motion of this kind of tether system in detail, the tether can be effectively modeled as flexible body and dealt by multibody dynamic analysis. In the analysis and modeling of flexible body of tether, large deformation and large displacement must be considered. Multibody dynamic analysis such as Absolute Nodal Coordinate Formulation with an introduction of the effect of damping force formulation can be used to describe the motion behavior of a flexible body. In this study, a parameter identification technique via an experimental approach is proposed in order to verify the modeling method. An example of swing-up control using the genetic algorithm control approach is performed through simulation and experiment. The validity of the model and availability of motion control based on multibody dynamics analysis are shown by comparison between numerical simulation and experiment

해양 작업 지원선의 자율 운항 및 설치 작업 지원을 위한 시뮬레이션 방법

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 조선해양공학과, 2019. 2. 노명일.Autonomous ships have gained a huge amount of interest in recent years, like their counterparts on land{autonomous cars, because of their potential to significantly lower the cost of operation, attract seagoing professionals and increase transportation safety. Technologies developed for the autonomous ships have potential to notably reduce maritime accidents where 75% cases can be attributed to human error and a significant proportion of these are caused by fatigue and attention deficit. However, developing a high-level autonomous system which can operate in an unstructured and unpredictable environment is still a challenging task. When the autonomous ships are operating in the congested waterway with other manned or unmanned vessels, the collision avoidance algorithm is the crucial point in keeping the safety of both the own ship and any encountered ships. Instead of developing new traffic rules for the autonomous ships to avoid collisions with each other, autonomous ships are expected to follow the existing guidelines based on the International Regulations for Preventing Collisions at Sea (COLREGs). Furthermore, when using the crane on the autonomous ship to transfer and install subsea equipment to the seabed, the heave and swaying phenomenon of the subsea equipment at the end of flexible wire ropes makes its positioning at an exact position is very difficult. As a result, an Anti-Motion Control (AMC) system for the crane is necessary to ensure the successful installation operation. The autonomous ship is highly relying on the effectiveness of autonomous systems such as autonomous path following system, collision avoidance system, crane control system and so on. During the previous two decades, considerable attention has been paid to develop robust autonomous systems. However, several are facing challenges and it is worthwhile devoting much effort to this. First of all, the development and testing of the proposed control algorithms should be adapted across a variety of environmental conditions including wave, wind, and current. This is one of the challenges of this work aimed at creating an autonomous path following and collision avoidance system in the ship. Secondly, the collision avoidance system has to comply with the regulations and rules in developing an autonomous ship. Thirdly, AMC system with anti-sway abilities for a knuckle boom crane remains problems regarding its under-actuated mechanism. At last, the performance of the control system should be evaluated in advance of the operation to perform its function successfully. In particular, such performance analysis is often very costly and time-consuming, and realistic conditions are typically impossible to establish in a testing environment. Consequently, to address these issues, we proposed a simulation framework with the following scenarios, which including the autonomous navigation scenario and crane operation scenario. The research object of this study is an autonomous offshore support vessel (OSV), which provides support services to offshore oil and gas field development such as offshore drilling, pipe laying, and oil producing assets (production platforms and FPSOs) utilized in EP (Exploration Production) activities. Assume that the autonomous OSV confronts an urgent mission under the harsh environmental conditions: on the way to an imperative offshore construction site, the autonomous OSV has to avoid target ships while following a predefined path. When arriving at the construction site, it starts to install a piece of subsea equipment on the seabed. So what technologies are needed, what should be invested for ensuring the autonomous OSV could robustly kilometers from shore, and how can an autonomous OSV be made at least as safe as the conventional ship. In this dissertation, we focus on the above critical activities for answering the above questions. In the general context of the autonomous navigation and crane control problem, the objective of this dissertation is thus fivefold: • Developing a COLREGs-compliant collision avoidance system. • Building a robust path following and collision avoidance system which can handle the unknown and complicated environment. • Investigating an efficient multi-ship collision avoidance method enable it easy to extend. • Proposing a hardware-in-the-loop simulation environment for the AHC system. • Solving the anti-sway problem of the knuckle boom crane on an autonomous OSV. First of all, we propose a novel deep reinforcement learning (RL) algorithm to achieve effective and efficient capabilities of the path following and collision avoidance system. To perform and verify the proposed algorithm, we conducted simulations for an autonomous ship under unknown environmental disturbance iiito adjust its heading in real-time. A three-degree-of-freedom dynamic model of the autonomous ship was developed, and the Line-of-sight (LOS) guidance system was used to converge the autonomous ship to follow the predefined path. Then, a proximal policy optimization (PPO) algorithm was implemented on the problem. By applying the advanced deep RL method, in which the autonomous OSV learns the best behavior through repeated trials to determine a safe and economical avoidance behavior in various circumstances. The simulation results showed that the proposed algorithm has the capabilities to guarantee collision avoidance of moving encountered ships while ensuring following a predefined path. Also, the algorithm demonstrated that it could manage complex scenarios with various encountered ships in compliance with COLREGs and have the excellent adaptability to the unknown, sophisticated environment. Next, the AMC system includes Anti-Heave Control (AHC) and Anti-Sway Control (ASC), which is applied to install subsea equipment in regular and irregular for performance analysis. We used the proportional-integral-derivative (PID) control method and the sliding mode control method respectively to achieve the control objective. The simulation results show that heave and sway motion could be significantly reduced by the proposed control methods during the construction. Moreover, to evaluate the proposed control system, we have constructed the HILS environment for the AHC system, then conducted a performance analysis of it. The simulation results show the AHC system could be evaluated effectively within the HILS environment. We can conclude that the proposed or adopted methods solve the problems issued in autonomous system design.해양 작업 지원선 (Offshore Support Vessel: OSV)의 경우 극한의 환경에도 불구하고 출항하여 해상에서 작업을 수행해야 하는 경우가 있다. 이러한 위험에의 노출을 최소화하기 위해 자율 운항에 대한 요구가 증가하고 있다. 여기서의 자율 운항은 선박이 출발지에서 목적지까지 사람의 도움 없이 이동함을 의미한다. 자율 운항 방법은 경로 추종 방법과 충돌 회피 방법을 포함한다. 우선, 운항 및 작업 중 환경 하중 (바람, 파도, 조류 등)에 대한 고려를 해야 하고, 국제 해상 충돌 예방 규칙 (Convention of the International Regulations for Preventing Collisions at Sea, COLREGs)에 의한 선박간의 항법 규정을 고려하여 충돌 회피 규칙을 준수해야 한다. 특히 연근해의 복잡한 해역에서는 많은 선박을 자동으로 회피할 필요가 있다. 기존의 해석적인 방법을 사용하기 위해서는 선박들에 대한 정확한 시스템 모델링이 되어야 하며, 그 과정에서 경험 (experience)에 의존하는 파라미터 튜닝이 필수적이다. 또한, 회피해야 할 선박 수가 많아질 경우 시스템 모델이 커지게 되고 계산 양과 계산 시간이 늘어나 실시간 적용이 어렵다는 단점이 있다. 또한, 경로 추종 및 충돌 회피를 포함하여 자율 운항 방법을 적용하기가 어렵다. 따라서 본 연구에서는 강화 학습 (Reinforcement Learning: RL) 기법을 이용하여 기존 해석적인 방법의 문제점을 극복할 수 있는 방법을 제안하였다. 경로를 추종하는 선박 (agent)은 외부 환경 (environment)과 상호작용하면서 학습을 진행한다. State S_0 (선박의 움직임과 관련된 각종 상태) 가지는 agent는 policy (현재 위치에서 어떤 움직임을 선택할 것인가)에 따라 action A_0 (움직일 방향) 취한다. 이에 environment는 agent의 다음 state S_1 을 계산하고, 그에 따른 보상 R_0 (해당 움직임의 적합성)을 결정하여 agent에게 전달한다. 이러한 작업을 반복하면서 보상이 최대가 되도록 policy를 학습하게 된다. 한편, 해상에서 크레인을 이용한 장비의 이동이나 설치 작업 시 위험을 줄이기 위해 크레인의 거동 제어에 대한 요구가 증가하고 있다. 특히 해상에서는 선박의 운동에 의해 크레인에 매달린 물체가 상하 동요 (heave)와 크레인을 기준으로 좌우 동요 (sway)가 발생하는데, 이러한 운동은 작업을 지연시키고, 정확한 위치에 물체를 놓지 못하게 하며, 자칫 주변 구조물과의 충돌을 야기할 수 있다. 이와 같은 동요를 최소화하는 Anti-Motion Control (AMC) 시스템은 Anti-Heave Control (AHC)과 Anti-Sway Control (ASC)을 포함한다. 본 연구에서는 해양 작업 지원선에 적합한 AMC 시스템의 설계 및 검증 방법을 연구하였다. 먼저 상하 동요를 최소화하기 위해 크레인의 와이어 길이를 능동적으로 조정하는 AHC 시스템을 설계하였다. 또한, 기존의 제어 시스템의 검증 방법은 실제 선박이나 해양 구조물에 해당 제어 시스템을 직접 설치하기 전에는 그 성능을 테스트하기가 힘들었다. 이를 해결하기 위해 본 연구에서는 Hardware-In-the-Loop Simulation (HILS) 기법을 활용하여 AHC 시스템의 검증 방법을 연구하였다. 또한, ASC 시스템을 설계할 때 제어 대상이 under-actuated 시스템이기 때문에 제어하기가 매우 어렵다. 따라서 본 연구에서는 sliding mode control 알고리즘을 이용하며 다관절 크레인 (knuckle boom crane)의 관절 (joint) 각도를 제어하여 좌우 동요를 줄일 수 있는 ASC 시스템을 설계하였다.Chapter 1 Introduction 1 1.1 Background and Motivation . . . . . . . . . . . . . . . . . . . . . 1 1.2 Requirements for Autonomous Operation . . . . . . . . . . . . . 5 1.2.1 Path Following for Autonomous Ship . . . . . . . . . . . . 5 1.2.2 Collision Avoidance for Autonomous Ship . . . . . . . . . 5 1.2.3 Anti-Motion Control System for Autonomous Ship . . . . 6 1.3 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.3.1 Related Work for Path Following System . . . . . . . . . 9 1.3.2 Related Work for Collision Avoidance System . . . . . . . 9 1.3.3 Related Work for Anti-Heave Control System . . . . . . . 13 1.3.4 Related Work for Anti-Sway Control System . . . . . . . 14 1.4 Configuration of Simulation Framework . . . . . . . . . . . . . . 16 1.4.1 Application Layer . . . . . . . . . . . . . . . . . . . . . . 16 1.4.2 Autonomous Ship Design Layer . . . . . . . . . . . . . . . 17 1.4.3 General Technique Layer . . . . . . . . . . . . . . . . . . 17 1.5 Contributions (Originality) . . . . . . . . . . . . . . . . . . . . . 19 Chapter 2 Theoretical Backgrounds 20 2.1 Maneuvering Model for Autonomous Ship . . . . . . . . . . . . . 20 2.1.1 Kinematic Equation for Autonomous Ship . . . . . . . . . 20 2.1.2 Kinetic Equation for Autonomous Ship . . . . . . . . . . 21 2.2 Multibody Dynamics Model for Knuckle Boom Crane of Autonomous Ship. . . 25 2.2.1 Embedding Techniques . . . . . . . . . . . . . . . . . . . . 25 2.3 Control System Design . . . . . . . . . . . . . . . . . . . . . . . . 31 2.3.1 Proportional-Integral-Derivative (PID) Control . . . . . . 31 2.3.2 Sliding Mode Control . . . . . . . . . . . . . . . . . . . . 31 2.4 Deep Reinforcement Learning Algorithm . . . . . . . . . . . . . . 34 2.4.1 Value Based Learning Method . . . . . . . . . . . . . . . 36 2.4.2 Policy Based Learning Method . . . . . . . . . . . . . . . 37 2.4.3 Actor-Critic Method . . . . . . . . . . . . . . . . . . . . . 41 2.5 Hardware-in-the-Loop Simulation . . . . . . . . . . . . . . . . . . 43 2.5.1 Integrated Simulation Method . . . . . . . . . . . . . . . 43 Chapter 3 Path Following Method for Autonomous OSV 46 3.1 Guidance System . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 3.1.1 Line-of-sight Guidance System . . . . . . . . . . . . . . . 46 3.2 Deep Reinforcement Learning for Path Following System . . . . . 50 3.2.1 Deep Reinforcement Learning Setup . . . . . . . . . . . . 50 3.2.2 Neural Network Architecture . . . . . . . . . . . . . . . . 56 3.2.3 Training Process . . . . . . . . . . . . . . . . . . . . . . . 58 3.3 Implementation and Simulation Result . . . . . . . . . . . . . . . 62 3.3.1 Implementation for Path Following System . . . . . . . . 62 3.3.2 Simulation Result . . . . . . . . . . . . . . . . . . . . . . 65 3.4 Comparison Results . . . . . . . . . . . . . . . . . . . . . . . . . 83 3.4.1 Comparison Result of PPO with PID . . . . . . . . . . . 83 3.4.2 Comparison Result of PPO with Deep Q-Network (DQN) 87 Chapter 4 Collision Avoidance Method for Autonomous OSV 89 4.1 Deep Reinforcement Learning for Collision Avoidance System . . 89 4.1.1 Deep Reinforcement Learning Setup . . . . . . . . . . . . 89 4.1.2 Neural Network Architecture . . . . . . . . . . . . . . . . 93 4.1.3 Training Process . . . . . . . . . . . . . . . . . . . . . . . 94 4.2 Implementation and Simulation Result . . . . . . . . . . . . . . . 95 4.2.1 Implementation for Collision Avoidance System . . . . . . 95 4.2.2 Simulation Result . . . . . . . . . . . . . . . . . . . . . . 100 4.3 Implementation and Simulation Result for Multi-ship Collision Avoidance Method . . . . . . . . . . . . . . . . . . . . . . . . . . 107 4.3.1 Limitations of Multi-ship Collision Avoidance Method - 1 107 4.3.2 Limitations of Multi-ship Collision Avoidance Method - 2 108 4.3.3 Implementation of Multi-ship Collision Avoidance Method 110 4.3.4 Simulation Result of Multi-ship Collision Avoidance Method 118 Chapter 5 Anti-Motion Control Method for Knuckle Boom Crane 129 5.1 Configuration of HILS for Anti-Heave Control System . . . . . . 129 5.1.1 Virtual Mechanical System . . . . . . . . . . . . . . . . . 132 5.1.2 Virtual Sensor and Actuator . . . . . . . . . . . . . . . . 138 5.1.3 Control System Design . . . . . . . . . . . . . . . . . . . . 141 5.1.4 Integrated Simulation Interface . . . . . . . . . . . . . . . 142 5.2 Implementation and Simulation Result of HILS for Anti-Heave Control System . . . . . . . . 145 5.2.1 Implementation of HILS for Anti-Heave Control System . 145 5.2.2 Simulation Result of HILS for Anti-Heave Control System 146 5.3 Validation of HILS for Anti-Heave Control System . . . . . . . . 159 5.3.1 Hardware Setup . . . . . . . . . . . . . . . . . . . . . . . 159 5.3.2 Comparison Result . . . . . . . . . . . . . . . . . . . . . . 161 5.4 Configuration of Anti-Sway Control System . . . . . . . . . . . . 162 5.4.1 Mechanical System for Knuckle Boom Crane . . . . . . . 162 5.4.2 Anti-Sway Control System Design . . . . . . . . . . . . . 165 5.4.3 Implementation and Simulation Result of Anti-Sway Control . . . . . . . . . . . . . . 168 Chapter 6 Conclusions and Future Works 176 Bibliography 178 Chapter A Appendix 186 국문초록 188Docto

Kinematic motion models based vessel state estimation to support advanced ship predictors

Advanced ship predictors can generally be considered as a vital part of the decision-making process of autonomous ships in the future, where the information on vessel maneuvering behavior can be used as the source of information to estimate current vessel motions and predict future behavior precisely. As a result, the navigation safety of autonomous vessels can be improved. In this paper, vessel maneuvering behavior consists of continuous-time system states of two kinematic motion models—the Curvilinear Motion Model (CMM) and Constant Turn Rate & Acceleration (CTRA) Model. Two state estimation algorithms—the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) are implemented on these two models with certain modifications so that they can be compatible with discrete-time measurements. Four scenarios, created by combining different models and algorithms, are implemented using simulated ship maneuvering data from a bridge simulator. These scenarios are then verified through the proposed stability and consistency tests. The simulation results show that the EKF tends to be unstable combined with the CMM. The estimates from the other three scenarios can generally be considered more stable and consistent, unless sudden actions or variations in vessel heading occurred during the simulation. The CTRA is also proven to be more robust compared to the CMM. As a result, a suitable combination of mathematical models and estimation filters can be considered to support advanced ship predictors in future ship navigation

Vibration Control for a Coupled Pitch- roll Ship Model Via a Negative Cubic Velocity Feedback Control

One of the most essential ship reactions to waves is roll motion. Due to the intricacy of ship wave interactions and their sensitivity, predicting such a reaction is extremely challenging. Because vibration motion is an undesirable occurrence, it must be removed, decreased, or controlled. A coupled Pitch- roll ship model with negative cubic velocity feedback control subjected to parametric excitations is premeditated and solved in this paper. The method of multiple time scales is applied to scrutinize the response of the two modes of the system neighbouring the simultaneous sub-harmonic, and internal resonance situation. Besides, the steady-state solution is determined through the Rung-Kutta Method (RKM) of fourth order. Stability of the steady state solution near this resonance case is discussed and studied applying Lyapunov’s first indirect method and Routh- Hurwitz criterion. The influences of the different parameters on the steady state solution are reconnoitred and discussed. The controller effects on the stability are clarified. Simulation results are accomplished with the help of MATLAB and Maple software programs

DESIGN CONTROL OF SURFACE MARINE VEHICLE USING DISTURBANCE COMPENSATING MODEL PREDICTIVE CONTROL (DC-MPC)

This research studied ship motion control by considering four degrees of freedom (DoF): yaw, roll, sway, and surge in which comprehensive mathematical modeling forming a nonlinear differential equation. Furthermore, this research also investigated solutions for fundamental yet challenging steering problems of ship maneuvering using advanced control method: Disturbance Compensating Model Predictive Control (DC-MPC) method, which based on Model Predictive Control (MPC). The DC-MPC allows optimizing a compensated control then consider sea waves as the environmental disturbances. Those sea waves influence the control and also becomes one of the constraints for the system. The simulation compared the varying condition of Horizon Prediction (Np) and another method showing that the DC-MPC can manage well the given disturbances while maneuvering in certain Horizon Prediction. The results revealed that the ship is stable and follows the desired trajector

Simulation of Multi-Layer-Liquid Sloshing Effects on Vessel Motions by Using Moving Particle Simulation

The coupling and interactions between ship motions and inner-liquid tank sloshing have been investigated by a coupled program between ship motion and sloshing analysis programs. For the sloshing program, Moving Particle Simulation (MPS), which is based on the Lagrangian approach, is used. This sloshing program is validated through comparisons with corresponding experimental results both qualitatively and quantitatively. This validated MPS method has been extended to multi-liquid systems by adding newly adopted models which are buoyancy-correction, surface tension, and boundary conditions at interfaces. Each new model is validated either mathematically or theoretically for comparison. Moreover, a new tracing method of interface particles is suggested by modifying the conventional free-surface searching method in MPS for a single-liquid system. The newly developed MPS for multi-liquid system has been tested for three-liquid sloshing and the obtained results have been compared with the corresponding experimental results. The verified MPS system is coupled with a ship motion program to investigate the sloshing effects on vessel motions. The coupled program was applied to two sloshing tanks, partially filled with fresh water, on a barge-type FPSO. The simulation results were compared with experiments by MARIN and showed good agreement. The most noticeable coupling effects on vessel motions show that the peak frequencies are split and shifted, especially in roll motions. Furthermore, comparison between cases of liquid- and rigid-cargo showed the importance of sloshing effects more clearly. The developed program was also applied to the multi-liquid sloshing problem to consider the wash-tank. In the case of the multi-liquid-layer, there are more than one sloshing natural frequencies, so the relevant physics can be much more complicated compared to the case of a single-liquid-tank. The oscillations of the interfaces have different amplitudes and frequencies. Since the wash-tank contained multi-liquids, short waves at the interface were generated due to Kelvin-Helmholtz instability and the phenomenon was successfully reproduced by the developed MPS-simulation technique