Rudder roll stabilization for ships

This paper describes the design of an autopilot for rudder roll stabilization for ships. This autopilot uses the rudder not only for course keeping but also for reduction of the roll. The system has a series of properties which make the controller design far from straightforward: the process has only one input (the rudder angle) and two outputs (the heading and the roll angle); the transfer from rudder to roll is non-minimum-phase; because large and high-frequency rudder motions are necessary, the non-linearities of the steering machine cannot be disregarded; the disturbances caused by the waves vary considerably in amplitude and frequency spectrum.\ud \ud In order to solve these problems a new approach to the LQG method has been developed. The control algorithms were tested by means of computer simulations, scale-model experiments and full-scale trials at sea. The results indicate that a rudder roll stabilization system is able to reduce the roll as well as a conventional fin stabilization system, while it requires less investments. Based on the results obtained in this project the Royal Netherlands Navy has decided to implement rudder roll stabilization on a series of ships under construction at this moment

MODEL REFERENCE ADAPTIVE CONTROL-BASED GENETIC ALGORITHM DESIGN FOR HEADING SHIP MOTION

In this paper, the heading control of a large ship is enhanced with a specific end goal, to check the unwanted impact of the waves on the actuator framework. The Nomoto model is investigated to describe the ship’s guiding progression. First and second order models are considered here. The viability of the models is examined based on the principal properties of the Nomoto model. Different controllers are proposed, these are Proportional Integral Derivative (PID), Linear Quadratic Regulator (LQR) and Model Reference Adaptive Control Genetic optimization Algorithm (MRAC-GA) for a ship heading control. The results show that the MRAC-GA controller provides the best results to satisfy the design requirements. The Matlab/Simulink tool is utilized to demonstrate the proposed arrangement in the control loop

Optimal Universal Controllers for Roll Stabilization

Roll stabilization is an important problem of ship motion control. This problem becomes especially difficult if the same set of actuators (e.g. a single rudder) has to be used for roll stabilization and heading control of the vessel, so that the roll stabilizing system interferes with the ship autopilot. Finding the "trade-off" between the concurrent goals of accurate vessel steering and roll stabilization usually reduces to an optimization problem, which has to be solved in presence of an unknown wave disturbance. Standard approaches to this problem (loop-shaping, LQG, $H_{\infty}$-control etc.) require to know the spectral density of the disturbance, considered to be a \colored noise". In this paper, we propose a novel approach to optimal roll stabilization, approximating the disturbance by a polyharmonic signal with known frequencies yet uncertain amplitudes and phase shifts. Linear quadratic optimization problems in presence of polyharmonic disturbances can be solved by means of the theory of universal controllers developed by V.A. Yakubovich. An optimal universal controller delivers the optimal solution for any uncertain amplitudes and phases. Using Marine Systems Simulator (MSS) Toolbox that provides a realistic vessel's model, we compare our design method with classical approaches to optimal roll stabilization. Among three controllers providing the same quality of yaw steering, OUC stabilizes the roll motion most efficiently

Ship Course Keeping Using Different Sliding Mode Controllers

This study addresses three sliding mode heading controllers for dealing with uncertain wave disturbances. A nonlinear steering model is derived, and the feedback linearization method is chosen to simplify the nonlinear system in this study. The adaptive method and disturbance observer technique are proposed for course keeping and ensuring robust performance of the time varying wave moment and actuator dynamics. Finally, the simulation results on a navy ship illustrate the effectiveness of the presented control algorithms for course keeping

Simulation Studies Relating to Rudder Roll Stabilization of a Container Ship Using Neural Networks

International audienceRRS (Rudder Roll Stabilization) of Ships is a difficult problem because of its associated non-linear dynamics, coupling effects and complex control requirements. This paper proposes a solution of this stabilization problem that is based on an ANN (Artificial Neural Network) controller. The controller has been trained using supervised learning. The simulation studies have been carried out using MATLAB and a non-linear model of a container ship. It has been demonstrated that the proposed controller regulates heading and also controls roll angle very successfully

A Study on the Automatic Ship Control Based on Adaptive Neural Networks

Recently, dynamic models of marine ships are often required to design advanced control systems. In practice, the dynamics of marine ships are highly nonlinear and are affected by highly nonlinear, uncertain external disturbances. This results in parametric and structural uncertainties in the dynamic model, and requires the need for advanced robust control techniques. There are two fundamental control approaches to consider the uncertainty in the dynamic model: robust control and adaptive control. The robust control approach consists of designing a controller with a fixed structure that yields an acceptable performance over the full range of process variations. On the other hand, the adaptive control approach is to design a controller that can adapt itself to the process uncertainties in such a way that adequate control performance is guaranteed. In adaptive control, one of the common assumptions is that the dynamic model is linearly parameterizable with a fixed dynamic structure. Based on this assumption, unknown or slowly varying parameters are found adaptively. However, structural uncertainty is not considered in the existing control techniques. To cope with the nonlinear and uncertain natures of the controlled ships, an adaptive neural network (NN) control technique is developed in this thesis. The developed neural network controller (NNC) is based on the adaptive neural network by adaptive interaction (ANNAI). To enhance the adaptability of the NNC, an algorithm for automatic selection of its parameters at every control cycle is introduced. The proposed ANNAI controller is then modified and applied to some ship control problems. Firstly, an ANNAI-based heading control system for ship is proposed. The performance of the ANNAI-based heading control system in course-keeping and turning control is simulated on a mathematical ship model using computer. For comparison, a NN heading control system using conventional backpropagation (BP) training methods is also designed and simulated in similar situations. The improvements of ANNAI-based heading control system compared to the conventional BP one are discussed. Secondly, an adaptive ANNAI-based track control system for ship is developed by upgrading the proposed ANNAI controller and combining with Line-of-Sight (LOS) guidance algorithm. The off-track distance from ship position to the intended track is included in learning process of the ANNAI controller. This modification results in an adaptive NN track control system which can adapt with the unpredictable change of external disturbances. The performance of the ANNAI-based track control system is then demonstrated by computer simulations under the influence of external disturbances. Thirdly, another application of the ANNAI controller is presented. The ANNAI controller is modified to control ship heading and speed in low-speed maneuvering of ship. Being combined with a proposed berthing guidance algorithm, the ANNAI controller becomes an automatic berthing control system. The computer simulations using model of a container ship are carried out and shows good performance. Lastly, a hybrid neural adaptive controller which is independent of the exact mathematical model of ship is designed for dynamic positioning (DP) control. The ANNAI controllers are used in parallel with a conventional proportional-derivative (PD) controller to adaptively compensate for the environmental effects and minimize positioning as well as tracking error. The control law is simulated on a multi-purpose supply ship. The results are found to be encouraging and show the potential advantages of the neural-control scheme.1. Introduction = 1 1.1 Background and Motivations = 1 1.1.1 The History of Automatic Ship Control = 1 1.1.2 The Intelligent Control Systems = 2 1.2 Objectives and Summaries = 6 1.3 Original Distributions and Major Achievements = 7 1.4 Thesis Organization = 8 2. Adaptive Neural Network by Adaptive Interaction = 9 2.1 Introduction = 9 2.2 Adaptive Neural Network by Adaptive Interaction = 11 2.2.1 Direct Neural Network Control Applications = 11 2.2.2 Description of the ANNAI Controller = 13 2.3 Training Method of the ANNAI Controller = 17 2.3.1 Intensive BP Training = 17 2.3.2 Moderate BP Training = 17 2.3.3 Training Method of the ANNAI Controller = 18 3. ANNAI-based Heading Control System = 21 3.1 Introduction = 21 3.2 Heading Control System = 22 3.3 Simulation Results = 26 3.3.1 Fixed Values of n and = 28 3.3.2 With adaptation of n and r = 33 3.4 Conclusion = 39 4. ANNAI-based Track Control System = 41 4.1 Introduction = 41 4.2 Track Control System = 42 4.3 Simulation Results = 48 4.3.1 Modules for Guidance using MATLAB = 48 4.3.2 M-Maps Toolbox for MATLAB = 49 4.3.3 Ship Model = 50 4.3.4 External Disturbances and Noise = 50 4.3.5 Simulation Results = 51 4.4 Conclusion = 55 5. ANNAI-based Berthing Control System = 57 5.1 Introduction = 57 5.2 Berthing Control System = 58 5.2.1 Control of Ship Heading = 59 5.2.2 Control of Ship Speed = 61 5.2.3 Berthing Guidance Algorithm = 63 5.3 Simulation Results = 66 5.3.1 Simulation Setup = 66 5.3.2 Simulation Results and Discussions = 67 5.4 Conclusion = 79 6. ANNAI-based Dynamic Positioning System = 80 6.1 Introduction = 80 6.2 Dynamic Positioning System = 81 6.2.1 Station-keeping Control = 82 6.2.2 Low-speed Maneuvering Control = 86 6.3 Simulation Results = 88 6.3.1 Station-keeping = 89 6.3.2 Low-speed Maneuvering = 92 6.4 Conclusion = 98 7. Conclusions and Recommendations = 100 7.1 Conclusion = 100 7.1.1 ANNAI Controller = 100 7.1.2 Heading Control System = 101 7.1.3 Track Control System = 101 7.1.4 Berthing Control System = 102 7.1.5 Dynamic Positioning System = 102 7.2 Recommendations for Future Research = 103 References = 104 Appendixes A = 112 Appendixes B = 11

Simulation studies relating to rudder roll stabilization of a container ship using neural networks

RRS (Rudder Roll Stabilization) of Ships is a difficult problem because of its associated non-linear dynamics, coupling effects and complex control requirements. This paper proposes a solution of this stabilization problem that is based on an ANN (Artificial Neural Network) controller. The controller has been trained using supervised learning. The simulation studies have been carried out using MATLAB and a non-linear model of a container ship. It has been demonstrated that the proposed controller regulates heading and also controls roll angle very successfully

Концептуальные основы адаптивных авторулевых

Проблематика. Роботу присвячено критичному аналізу літератури, що охоплює головні аспекти створення адаптивних систем керування рухом судна. Мета дослідження. Метою роботи є визначення перспективних напрямів досліджень у галузі створення адаптивних систем керування рухом судна. Методика реалізації. Проведено аналіз існуючих підходів до ідентифікації параметрів моделі судна (зокрема, ідентифікації на зиґзаґу, на циркуляції та за допомогою калманівської фільтрації), визначено переваги і недоліки цих методів, що можуть бути покладені в основу створення адаптивних автостернових. Наведено критичний аналіз підходів до керування судном за допомогою класичних та новітніх методів автоматичного керування об’єктами, зокрема параметричного настроювання класичних ПІД-регуляторів, перемикання регуляторів, застосування нелінійних регуляторів – лінійно-квадратичних (LQ), “ковзного режиму” (sliding mode), а також штучного інтелекту – нейромереж, нечіткої логіки та гібридних підходів. Окремо в огляді наведено аналіз розробок вітчизняних авторів, присвячених розробці адаптивних автостернових та адаптивному керуванню рухом судна. Результати дослідження. В результаті аналізу літературних джерел визначено перспективні напрями досліджень у галузі створення адаптивних систем керування рухом судна. Висновки. Перспективними напрямами досліджень є: 1) розробка нових підходів до ідентифікації параметрів моделі руху судна та збурень, що діють на нього; 2) застосування методів штучного інтелекту, зокрема нечіткої логіки та нейромереж, до адаптивного керування судном; 3) побудова адаптивних нелінійних систем керування рухом судна.Background. The paper is devoted to critical analysis of literature that covers the major aspects of adaptive ship motion control systems. Objective. The objective of a study is identifying the promising areas of research in the field of adaptive ship motion control. Methods. The analysis of existing approaches to ship model parameters identification (including identification during zig-zag motion, during circulation and identification using Kalman filtering) is done; advantages and disadvantages of those methods are determined. The methods mentioned can be used as a basis for creating adaptive gyropilots. A critical review of approaches to ship control by means of classical and modern methods of automatic control, including the parametric adjustment of classic PID regulators, switching of regulators, use of nonlinear regulators — linear-quadratic (LQ), sliding mode regulators, and artificial intelligence — neural networks, fuzzy logic and hybrid approaches, is done. Separately, in the survey analysis of papers of Ukrainian authors, which are devoted to the development of adaptive gyropilots and adaptive ship motion control, is presented. Results. As a result of literature survey, prospective areas of studies in the field of adaptive ship control are determined. Conclusions. Most promising research areas are: 1) development of novel approaches to the identification of the vessel model parameters and disturbances acting on it; 2) application of artificial intelligence, including fuzzy logic and neural networks, to adaptive ship control methods; 3) development of adaptive nonlinear systems for ship motion control.Проблематика. Работа посвящена критическому анализу литературы, охватывающей основные аспекты создания адаптивных систем управления движением судна. Цель исследования. Цель работы – определение перспективных направлений исследований в области создания адаптивных систем управления движением судна. Методика реализации. Проведен анализ существующих подходов к идентификации параметров модели судна (в частности, идентификации на зигзаге, на циркуляции и с помощью калмановской фильтрации), определены преимущества и недостатки этих методов, которые могут быть положены в основу создания адаптивных авторулевых. Приведен критический анализ подходов к управлению судном с помощью классических и новых методов автоматического управления объектами, в частности параметрической настройки классических ПИД-регуляторов, переключения регуляторов, применения нелинейных регуляторов – линейно-квадратичных (LQ), “скользящего режима” (sliding mode), а также искусственного интеллекта – нейросетей, нечеткой логики и гибридных подходов. Отдельно в обзоре приведен анализ разработок отечественных авторов, посвященных разработке адаптивных авторулевых и адаптивному управлению движением судна. Результаты исследования. В результате анализа литературных источников определены перспективные направления исследований в области создания адаптивных систем управления движением судна. Выводы. Перспективными направлениями исследований являются: 1) разработка новых подходов к идентификации параметров модели движения судна и действующих на него возмущений; 2) применение методов искусственного интеллекта, в частности нечеткой логики и нейронных сетей, к адаптивному управлению судном; 3) построение адаптивных нелинейных систем управления движением судна