한국해양대학교

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Performance Improvement on Path following and Autopilot of ship using Unknown Disturbance Estimation and Separation Principle

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dc.contributor.advisor 김종화 -
dc.contributor.author 김민규 -
dc.date.accessioned 2019-12-16T02:53:08Z -
dc.date.available 2019-12-16T02:53:08Z -
dc.date.issued 2018 -
dc.identifier.uri http://repository.kmou.ac.kr/handle/2014.oak/11661 -
dc.identifier.uri http://kmou.dcollection.net/common/orgView/200000014114 -
dc.description.abstract The most important thing in ship operation is to assure the stability of ship from sinking or collision that may occur during navigation, and is for ship to follow the designated route accurately even when environmental disturbances (current, wave, and wind) are applied. Accurate path following minimizes the loss of propulsion energy of the ship and allows it to reach the destination in the shortest time. The PD type autopilot system, which is widely used in ships, has an overshoot when the rudder angle is large and acts as a cause that the position of ship deviates from the route. And also, the change of the rudder angle becomes rough so that energy loss of the ship is increased. To overcome these disadvantages, a velocity type fuzzy PID autopilot system is applied. However autopilot system is affected by noise, so that the performance of the autopilot system is degraded. To compensate for this, the separation principle using the Kalman filter is applied to improve the performance. By the way, in actual navigation of the ship, if the forces and moments generated by environmental disturbances are applied to the ship, the ship can not follow the designated route correctly and deviates from the route. In order to compensate for this, if the existence of unknown disturbances are judged by using the innovation characteristic of the Kalman filer, the forces and moments are estimated in the fuzzy disturbance estimator and converted into the thrust and rudder angle controlling the ship.|선박 운항에 있어서 가장 중요한 사항은 항해중 발생할 수 있는 침몰 또는 충돌로부터 선박의 안정성을 확보하고 환경적 외란(파도, 바람, 해류등)이 인가된 경우에도 정해진 항로를 정확하게 추종하는 것이다. 정확한 항로 추종은 선박의 추진 에너지의 손실을 최소화 하며 목적지까지 최단시간에 도달하도록 한다. 현재 선박에 많이 사용되고 있는 회두각 유지 제어기로서 PD형 제어기는 변침각이 큰 경우 오버슈트를 발생시켜 항로에서 벗어나는 원인으로 작용하고 또한 잦은 조타와 변침각의 변화가 거칠게 일어나 선박의 에너지 손실을 증가시킨다. 이와 같은 단점을 보완하기 위해서 속도형 퍼지 PID 오토파일럿 시스템을 적용하였다. 또한 실제의 오토파일럿 시스템은 잡음의 영향을 받아 성능이 저하되는데 이를 보완하기 위해서 Kalman 필터를 이용한 분리원리를 적용하여 성능을 개선시켰다. 그러나 실제 선박이 운항함에 있어서는 환경적 외란으로 인해 발생하는 힘과 모멘트가 선박에 인가되어 선박은 정해진 항로를 정확하게 추종할 수 없고 항로에서 이탈하게 된다. 이를 보완하기 위해서 Kalman 필터의 이노베이션 특성을 이용하여 미지의 외란의 존재가 판단되었으면 이를 퍼지 외란 추정기에서 힘과 모멘트를 추정하고, 이를 실제 선박을 제어하는 추력과 타각으로 변환하여 선박이 정해진 항로를 크게 벗어나지 않고 추종하도록 하였다. -
dc.description.tableofcontents List of Figures ⅴ List of Tables ⅷ Abstract ⅸ Chapter 1 Introduction 1 1.1 Background of a research 1 1.2 Organization of thesis 3 Chapter 2 Modeling of Ship 5 2.1 Body-fixed coordinate frame and definition of notation used for ship motion 5 2.2 6 DOF nonlinear ship equation of motion 6 2.2.1 Rigid-body ship equation of motion 7 2.2.2 Hydrodynamic ship equation of motion 10 2.3 3 DOF nonlinear ship equation of motion 15 2.3.1 Separation of speed equation and steering equation 16 2.4 Linearlization on 3 DOF nonlinear ship equation of motion 17 2.4.1 Linear forward direction motion model proposed by Blanke 19 2.4.2 Linear steering equation suggested by Davison and Schiff 20 2.5 Combination of linear speed equation with linear steering equation 22 2.6 Simulation on path following of ship 23 2.6.1 Simulation condition 23 2.6.2 Simulation results on path following (PD autopilot) 25 Chapter 3 Velocity type fuzzy PID autopilot system 28 3.1 Velocity type fuzzy PID autopilot system 29 3.1.1 Fuzzification algorithm 30 3.1.2 Fuzzy control rule 31 3.2 Performance verification of velocity type fuzzy PID autopilot 35 3.2.1 Course change simulation 35 3.2.2 Path following simulation(Velocity type fuzzy PID autopilot) 39 Chapter 4 Environmental disturbances 41 4.1 Current 41 4.1.1 Current generation model 41 4.1.2 Current generation simulations 42 4.1.3 Effect of current on the ship 43 4.2 Wind 44 4.2.1 Wind generation 44 4.2.2 Effects of wind on ship 45 4.3 Wave 48 4.3.1 Wave generation model 48 4.3.2 Wave generation simulations 51 4.3.3 Effect of wave on ship 52 4.4 3 DOF ship equation of motion combined with current, wind and wave model 53 4.5 Simulation of ship motion under disturbances 54 Chapter 5 Velocity type fuzzy PID autopilot system using separation principle based on Kalman filter 63 5.1 Linear discrete stochastic state space model of ship including white Gaussian noise 63 5.2 Kalman filter state estimation algorithm in discrete timesystem 66 5.3 Velocity type fuzzy PID autopilot system based on Kalman filter 67 5.4 Linear discrete stochastic space state model of shipincluding unknown disturbances and white Gaussian noises 71 Chapter 6 Fuzzy disturbance estimator based on the innovation process of Kalman filter 74 6.1 How to judge the existence of unknown disturbances 74 6.2 Fuzzy disturbance estimation algorithm 75 6.2.1 Fuzzification algorithm 77 6.2.2 Fuzzy estimation rule 78 6.2.3 Kalman filter based state estimation algorithm with fuzzy disturbanceestimation algorithm 81 6.3 Proposal of fuzzy PID autopilot system based on Kalman filter with unknown disturbance estimator 82 Chapter 7 Conversion of the estimated value of unknown disturbances to propeller thrust and rudder angle 89 Chapter 8 Conclusion 95 Reference -
dc.format.extent 110 -
dc.language eng -
dc.publisher 한국해양대학교 해양과학기술전문대학원 -
dc.rights 한국해양대학교 논문은 저작권에 의해 보호받습니다. -
dc.title Performance Improvement on Path following and Autopilot of ship using Unknown Disturbance Estimation and Separation Principle -
dc.type Dissertation -
dc.date.awarded 2018-02 -
dc.contributor.alternativeName Kim MinKyu -
dc.contributor.department 해양과학기술전문대학원 해양과학기술융합학과 -
dc.contributor.affiliation 한국해양대학교 해양과학기술전문대학원 해양과학기술융합학과 -
dc.description.degree Master -
dc.subject.keyword Kalman Filter, Velocity Type Fuzzy PID Autopilot System, Fuzzy Disturbance Estimator, Separation Principle using Kalman Filter -
dc.title.translated 미지의 외란 추정과 분리원리를 이용한 선박의 항로 추종 및 오토파일럿 성능 개선 -
dc.identifier.holdings 000000001979▲200000000139▲200000014114▲ -
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