Experimental Validation Of An Integrated Guidance And Control System For Marine Surface Vessels

Autonomous operation of marine surface vessels is vital for minimizing human errors and providing efficient operations of ships under varying sea states and environmental conditions which is complicated by the highly nonlinear dynamics of marine surface vessels. To deal with modelling imprecision and unpredictable disturbances, the sliding mode methodology has been employed to devise a heading and a surge displacement controller. The implementation of such a controller necessitates the availability of all state variables of the vessel. However, the measured signals in the current study are limited to the global X and Y positioning coordinates of the boat that are generated by a GPS system. Thus, a nonlinear observer, based on the sliding mode methodology, has been implemented to yield accurate estimates of the state variables in the presence of both structured and unstructured uncertainties. Successful autonomous operation of a marine surface vessel requires a holistic approach encompassing a navigation system, robust nonlinear controllers and observers. Since the overwhelming majority of the experimental work on autonomous marine surface vessels was not conducted in truly uncontrolled real-world environments. The first goal of this work was to experimentally validate a fully-integrated LOS guidance system with a sliding mode controller and observer using a 16’ Tracker Pro Guide V-16 aluminium boat with a 60 hp. Mercury outboard motor operating in the uncontrolled open-water environment of Lake St. Clair, Michigan. The fully integrated guidance and controller-observer system was tested in a model-less configuration, whereby all information provided from the vessel’s nominal model have been ignored. The experimental data serves to demonstrate the robustness and good tracking characteristics of the fully-integrated guidance and controller/observer system by overcoming the large errors induced at the beginning of each segment and converging the boat to the desired trajectory in spite of the presence of environmental disturbances. The second focus of this work was to combine a collision avoidance method with the guidance system that accounted for “International Regulations for Prevention of Collisions at Sea” abbreviated as COLREGS. This new system then needed to be added into the existing architecture. The velocity obstacles method was selected as the base to build upon and additional restrictions were incorporated to account for these additional rules. This completed system was then validated with a software in the loop simulation

Automatic Control and Routing of Marine Vessels

Due to the intensive development of the global economy, many problems are constantly emerging connected to the safety of ships’ motion in the context of increasing marine traffic. These problems seem to be especially significant for the further development of marine transportation services, with the need to considerably increase their efficiency and reliability. One of the most commonly used approaches to ensuring safety and efficiency is the wide implementation of various automated systems for guidance and control, including such popular systems as marine autopilots, dynamic positioning systems, speed control systems, automatic routing installations, etc. This Special Issue focuses on various problems related to the analysis, design, modelling, and operation of the aforementioned systems. It covers such actual problems as tracking control, path following control, ship weather routing, course keeping control, control of autonomous underwater vehicles, ship collision avoidance. These problems are investigated using methods such as neural networks, sliding mode control, genetic algorithms, L2-gain approach, optimal damping concept, fuzzy logic and others. This Special Issue is intended to present and discuss significant contemporary problems in the areas of automatic control and the routing of marine vessels

Application of fuzzy controllers in automatic ship motion control systems

Automatic ship heading control is a part of the automatic navigation system. It is charged with the task of maintaining the actual ship’s course angle or actual ship’s course without human intervention in accordance with the set course or setting parameter and maintaining this condition under the effect of disturbing influences. Thus, the corrective influence on deviations from a course can be rendered by the position of a rudder or controlling influence that leads to the rotary movement of a vessel around a vertical axis that represents a problem, which can be solved with the use of fuzzy logic. In this paper, we propose to consider the estimation of the efficiency of fuzzy controllers in systems of automatic control of ship movement, obtained by analysis of a method of the formalized record of a logic conclusion and structure of the fuzzy controller. The realization of this allows to carry out effective stabilization of a course angle of a vessel taking into account existing restrictions

Non-Linear Robust Observers For Systems With Non-Collocated Sensors And Actuators

Challenges in controlling highly nonlinear systems are not limited to the development of sophisticated control algorithms that are tolerant to significant modeling imprecision and external disturbances. Additional challenges stem from the implementation of the control algorithm such as the availability of the state variables needed for the computation of the control signals, and the adverse effects induced by non-collocated sensors and actuators. The present work investigates the adverse effects of non-collocated sensors and actuators on the phase characteristics of flexible structures and the ensuing implications on the performance of structural controllers. Two closed-loop systems are considered and their phase angle contours have been generated as functions of the normalized sensor location and the excitation frequency. These contours were instrumental in the development of remedial actions for rendering structural controllers immune to the detrimental effects of non-collocated sensors and actuators. Moreover, the current work has focused on providing experimental validation for the robust performances of a self-tuning observer and a sliding mode observer. The observers are designed based on the variable structure systems theory and the self-tuning fuzzy logic scheme. Their robustness and self-tuning characteristics allow one to use an imprecise model of the system and eliminate the need for the extensive tuning associated with a fixed rule-based expert fuzzy inference system. The first phase of the experimental work was conducted in a controlled environment on a flexible spherical robotic manipulator whose natural frequencies are configuration-dependent. Both controllers have yielded accurate estimates of the required state variables in spite of significant modeling imprecision. The observers were also tested under a completely uncontrolled environment, which involves a 16-ft boat operating in open-water under different sea states. Such an experimental work necessitates the development of a supervisory control algorithm to perform PTP tasks, prescribed throttle arm and steering tasks, surge speed and heading tracking tasks, or recovery maneuvers. This system has been implemented herein to perform prescribed throttle arm and steering control tasks based on estimated rather than measured state variables. These experiments served to validate the observers in a completely uncontrolled environment and proved their viability as reliable techniques for providing accurate estimates for the required state variables

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

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

Intelligent Control Strategies for an Autonomous Underwater Vehicle

The dynamic characteristics of autonomous underwater vehicles (AUVs) present a control problem that classical methods cannot often accommodate easily. Fundamentally, AUV dynamics are highly non-linear, and the relative similarity between the linear and angular velocities about each degree of freedom means that control schemes employed within other flight vehicles are not always applicable. In such instances, intelligent control strategies offer a more sophisticated approach to the design of the control algorithm. Neurofuzzy control is one such technique, which fuses the beneficial properties of neural networks and fuzzy logic in a hybrid control architecture. Such an approach is highly suited to development of an autopilot for an AUV. Specifically, the adaptive network-based fuzzy inference system (ANFIS) is discussed in Chapter 4 as an effective new approach for neurally tuning course-changing fuzzy autopilots. However, the limitation of this technique is that it cannot be used for developing multivariable fuzzy structures. Consequently, the co-active ANFIS (CANFIS) architecture is developed and employed as a novel multi variable AUV autopilot within Chapter 5, whereby simultaneous control of the AUV yaw and roll channels is achieved. Moreover, this structure is flexible in that it is extended in Chapter 6 to perform on-line control of the AUV leading to a novel autopilot design that can accommodate changing vehicle pay loads and environmental disturbances. Whilst the typical ANFIS and CANFIS structures prove effective for AUV control system design, the well known properties of radial basis function networks (RBFN) offer a more flexible controller architecture. Chapter 7 presents a new approach to fuzzy modelling and employs both ANFIS and CANFIS structures with non-linear consequent functions of composite Gaussian form. This merger of CANFIS and a RBFN lends itself naturally to tuning with an extended form of the hybrid learning rule, and provides a very effective approach to intelligent controller development.The Sea Systems and Platform Integration Sector, Defence Evaluation and Research Agency, Winfrit