Editorial dynamics and control of aerospace and vertical transportation systems

This Special Issue presents a selection of papers initially presented at the 11th International Conference on Vibration Problems (ICOVP-2013), held from 9 to 12 September 2013 in Lisbon, Portugal. The main topics of this Special Issue are linear and, mainly, nonlinear dynamics, chaos and control of systems and structures and their applications in different field of science and engineering. According to the goal of the Special Issue, the selected contributions are divided into three major parts: “Vibration Problems in Vertical Transportation Systems”, “Nonlinear Dynamics, Chaos and Control of Elastic Structures” and “New Strategies and Challenges for Aerospace and Ocean Structures Dynamics and Control”.

New approach for power transformer protection based on intelligent hybrid systems

A power transformer needs continuous monitoring and fast protection as it is a very expensive piece of equipment and an essential element in an electrical power system. The most common protection technique used is the percentage differential logic, which provides discrimination between an internal fault and different operating conditions. Unfortunately, there are some operating conditions of power transformers that can mislead the conventional protection affecting the power system stability negatively. This study proposes the development of a new algorithm to improve the protection performance by using fuzzy logic, artificial neural networks and genetic algorithms. An electrical power system was modelled using Alternative Transients Program software to obtain the operational conditions and fault situations needed to test the algorithm developed, as well as a commercial differential relay. Results show improved reliability, as well as a fast response of the proposed technique when compared with conventional ones.

Framework for risk assessment for maritime transportation systems

Maritime accidents involving ships carrying passengers may pose a high risk with respect to human casualties. For effective risk mitigation, an insight into the process of risk escalation is needed. This requires a proactive approach when it comes to risk modelling for maritime transportation systems. Most of the existing models are based on historical data on maritime accidents, and thus they can be considered reactive instead of proactive. This paper introduces a systematic, transferable and proactive framework estimating the risk for maritime transportation systems, meeting the requirements stemming from the adopted formal definition of risk. The framework focuses on ship-ship collisions in the open sea, with a RoRo/Passenger ship (RoPax) being considered as the struck ship. First, it covers an identification of the events that follow a collision between two ships in the open sea, and, second, it evaluates the probabilities of these events, concluding by determining the severity of a collision. The risk framework is developed with the use of Bayesian Belief Networks and utilizes a set of analytical methods for the estimation of the risk model parameters. The model can be run with the use of GeNIe software package. Finally, a case study is presented, in which the risk framework developed here is applied to a maritime transportation system operating in the Gulf of Finland (GoF). The results obtained are compared to the historical data and available models, in which a RoPax was involved in a collision, and good agreement with the available records is found.

Making Transport Safer: V2V-Based Automated Emergency Braking System

An important goal in the field of intelligent transportation systems (ITS) is to provide driving aids aimed at preventing accidents and reducing the number of traffic victims. The commonest traffic accidents in urban areas are due to sudden braking that demands a very fast response on the part of drivers. Attempts to solve this problem have motivated many ITS advances including the detection of the intention of surrounding cars using lasers, radars or cameras. However, this might not be enough to increase safety when there is a danger of collision. Vehicle to vehicle communications are needed to ensure that the other intentions of cars are also available. The article describes the development of a controller to perform an emergency stop via an electro-hydraulic braking system employed on dry asphalt. An original V2V communication scheme based on WiFi cards has been used for broadcasting positioning information to other vehicles. The reliability of the scheme has been theoretically analyzed to estimate its performance when the number of vehicles involved is much higher. This controller has been incorporated into the AUTOPIA program control for automatic cars. The system has been implemented in Citroën C3 Pluriel, and various tests were performed to evaluate its operation.

Genetic fuzzy-based steering wheel controller using a mass-produced car

Intelligent Transportation Systems (ITS) cover a broad range of methods and technologies that provide answers to many problems of transportation. Unmanned control of the steering wheel is one of the most important challenges facing researchers in this area. This paper presents a method to adjust automatically a fuzzy controller to manage the steering wheel of a mass-produced vehicle to reproduce the steering of a human driver. To this end, information is recorded about the car's state while being driven by human drivers and used to obtain, via genetic algorithms, appropriate fuzzy controllers that can drive the car in the way that humans do. These controllers have satisfy two main objectives: to reproduce the human behavior, and to provide smooth actions to ensure comfortable driving. Finally, the results of automated driving on a test circuit are presented, showing both good route tracking (similar to the performance obtained by persons in the same task) and smooth driving.

Arquitectura de Control para la Conducción Autónoma de Vehículos en Entornos Urbanos y Autovías

Hoy en día, el desarrollo tecnológico en el campo de los sistemas inteligentes de transporte (ITS por sus siglas en inglés) ha permitido dotar a los vehículos con diversos sistemas de ayuda a la conducción (ADAS, del inglés advanced driver assistance system), mejorando la experiencia y seguridad de los pasajeros, en especial del conductor. La mayor parte de estos sistemas están pensados para advertir al conductor sobre ciertas situaciones de riesgo, como la salida involuntaria del carril o la proximidad de obstáculos en el camino. No obstante, también podemos encontrar sistemas que van un paso más allá y son capaces de cooperar con el conductor en el control del vehículo o incluso relegarlos de algunas tareas tediosas. Es en este último grupo donde se encuentran los sistemas de control electrónico de estabilidad (ESP - Electronic Stability Program), el antibloqueo de frenos (ABS - Anti-lock Braking System), el control de crucero (CC - Cruise Control) y los más recientes sistemas de aparcamiento asistido. Continuando con esta línea de desarrollo, el paso siguiente consiste en la supresión del conductor humano, desarrollando sistemas que sean capaces de conducir un vehículo de forma autónoma y con un rendimiento superior al del conductor. En este trabajo se presenta, en primer lugar, una arquitectura de control para la automatización de vehículos. Esta se compone de distintos componentes de hardware y software, agrupados de acuerdo a su función principal. El diseño de la arquitectura parte del trabajo previo desarrollado por el Programa AUTOPIA, aunque introduce notables aportaciones en cuanto a la eficiencia, robustez y escalabilidad del sistema. Ahondando un poco más en detalle, debemos resaltar el desarrollo de un algoritmo de localización basado en enjambres de partículas. Este está planteado como un método de filtrado y fusión de la información obtenida a partir de los distintos sensores embarcados en el vehículo, entre los que encontramos un receptor GPS (Global Positioning System), unidades de medición inercial (IMU – Inertial Measurement Unit) e información tomada directamente de los sensores embarcados por el fabricante, como la velocidad de las ruedas y posición del volante. Gracias a este método se ha conseguido resolver el problema de la localización, indispensable para el desarrollo de sistemas de conducción autónoma. Continuando con el trabajo de investigación, se ha estudiado la viabilidad de la aplicación de técnicas de aprendizaje y adaptación al diseño de controladores para el vehículo. Como punto de partida se emplea el método de Q-learning para la generación de un controlador borroso lateral sin ningún tipo de conocimiento previo. Posteriormente se presenta un método de ajuste on-line para la adaptación del control longitudinal ante perturbaciones impredecibles del entorno, como lo son los cambios en la inclinación del camino, fricción de las ruedas o peso de los ocupantes. Para finalizar, se presentan los resultados obtenidos durante un experimento de conducción autónoma en carreteras reales, el cual se llevó a cabo en el mes de Junio de 2012 desde la población de San Lorenzo de El Escorial hasta las instalaciones del Centro de Automática y Robótica (CAR) en Arganda del Rey. El principal objetivo tras esta demostración fue validar el funcionamiento, robustez y capacidad de la arquitectura propuesta para afrontar el problema de la conducción autónoma, bajo condiciones mucho más reales a las que se pueden alcanzar en las instalaciones de prueba. ABSTRACT Nowadays, the technological advances in the Intelligent Transportation Systems (ITS) field have led the development of several driving assistance systems (ADAS). These solutions are designed to improve the experience and security of all the passengers, especially the driver. For most of these systems, the main goal is to warn drivers about unexpected circumstances leading to risk situations such as involuntary lane departure or proximity to other vehicles. However, other ADAS go a step further, being able to cooperate with the driver in the control of the vehicle, or even overriding it on some tasks. Examples of this kind of systems are the anti-lock braking system (ABS), cruise control (CC) and the recently commercialised assisted parking systems. Within this research line, the next step is the development of systems able to replace the human drivers, improving the control and therefore, the safety and reliability of the vehicles. First of all, this dissertation presents a control architecture design for autonomous driving. It is made up of several hardware and software components, grouped according to their main function. The design of this architecture is based on the previous works carried out by the AUTOPIA Program, although notable improvements have been made regarding the efficiency, robustness and scalability of the system. It is also remarkable the work made on the development of a location algorithm for vehicles. The proposal is based on the emulation of the behaviour of biological swarms and its performance is similar to the well-known particle filters. The developed method combines information obtained from different sensors, including GPS, inertial measurement unit (IMU), and data from the original vehicle’s sensors on-board. Through this filtering algorithm the localization problem is properly managed, which is critical for the development of autonomous driving systems. The work deals also with the fuzzy control tuning system, a very time consuming task when done manually. An analysis of learning and adaptation techniques for the development of different controllers has been made. First, the Q-learning –a reinforcement learning method– has been applied to the generation of a lateral fuzzy controller from scratch. Subsequently, the development of an adaptation method for longitudinal control is presented. With this proposal, a final cruise control controller is able to deal with unpredictable environment disturbances, such as road slope, wheel’s friction or even occupants’ weight. As a testbed for the system, an autonomous driving experiment on real roads is presented. This experiment was carried out on June 2012, driving from San Lorenzo de El Escorial up to the Center for Automation and Robotics (CAR) facilities in Arganda del Rey. The main goal of the demonstration was validating the performance, robustness and viability of the proposed architecture to deal with the problem of autonomous driving under more demanding conditions than those achieved on closed test tracks.

Desarrollo de algoritmos de toma de decisión aplicados a las ciudades inteligentes

Este trabajo se enmarca dentro del ámbito de las Ciudades Inteligentes. Una Ciudad Inteligente se puede definir como aquella ciudad que usa las tecnologías de la información y las comunicaciones para hacer que tanto su infraestructura crítica, como sus componentes y servicios públicos ofrecidos sean más interactivos, eficientes y los ciudadanos puedan ser más conscientes de ellos. Se trata de un concepto emergente que presenta una serie de retos de diseño que se deben abordar. Dos retos importantes son la variabilidad del contexto con el tiempo y la incertidumbre en la información del contexto. Una parte fundamental de estos sistemas, y que permite abordar estos retos, son los mecanismos de toma de decisión. Estos mecanismos permiten a los sistemas modificar su comportamiento en función de los cambios que detecten en su contexto, de manera que puedan adaptarse y responder adecuadamente a la situación en cada momento. Este trabajo tiene como objetivo el desarrollo de algoritmos de toma de decisión en el marco de las Ciudades Inteligentes. En particular, se ha diseñado e implementado, utilizando el software MATLAB, un algoritmo de toma de decisión que aborda los retos mencionados y que se puede aplicar en una de las áreas que engloban las Ciudades Inteligentes: los Sistemas Inteligentes de Transporte. Este proyecto se estructura fundamentalmente en dos partes: una parte teórica y una parte práctica. En la parte teórica se trata de proporcionar al lector nociones básicas sobre los conceptos de Ciudad Inteligente y Sistemas Inteligentes de Transporte, así como de la toma de decisión. También se explican los pasos del procedimiento de la toma de decisión y se proporciona un estado del arte de los algoritmos de toma de decisión existentes. Por otro lado, la segunda parte de este proyecto es totalmente original, y en ella el autor propone un algoritmo de toma de decisión para ser aplicado en el ámbito de los Sistemas Inteligentes de Transporte y desarrolla la implementación en MATLAB del algoritmo mencionado. Por último, para demostrar su funcionamiento, se valida el algoritmo en un escenario de aplicación consistente en un sistema inteligente de gestión del tráfico. ABSTRACT. This master thesis is framed under Smart Cities environment. A Smart City can be defined as the use of Information and Communication Technologies to make the critical infrastructure components and services of a city more intelligent, interconnected and efficient and citizens can be also more aware of them. Smart City is a new concept which presents a novel set of design challenges that must be addressed. Two important challenges are the changeable context and the uncertainty of context information. One of the essential parts of Smart Cities, which enables to address these challenges, are decision making mechanisms. Based on the information collected of the context, these systems can be configured to change its behavior whenever certain changes are detected, so that they can adapt themselves and response to the current situation properly. This master thesis is aimed at developing decision making algorithms under Smart Cities framework. In particular, a decision making algorithm which addresses the abovementioned challenges and that can be applied to one of the main categories of Smart Cities, named Intelligent Transportation Systems, has been designed and implemented. To do so, MATLAB software has been used. This project is mainly structured in two parts: a theoretical part and a practical part. In theoretical part, basic ideas about the concept of Smart Cities and Intelligent Transportation Systems are given, as well as the concept of decision making. The steps of the decision making procedure are also explained and a state of the art of existing decision making algorithms is provided. On the other hand, the second part of this project is totally original. In this part, the author propose a decision making algorithm that can be applied to Intelligent Transportation Systems and develops the implementation of the algorithm in MATLAB. Finally, to show the operation of the algorithm, it is validated in an application scenario consisting in a smart traffic management system.

Intelligent vehicle initiative: business plan.

Mode of access: Internet.

Advanced public transportation systems: the state of the art update '98.

Federal Transit Administration, Washington, D.C.

Evaluation of the Intelligent Cruise Control System. Volume I: study results.

National Highway Traffic Safety Administration, Washington, D.C.

Responsive multimodal transportation management strategies and IVHS. Final report.

Federal Highway Administration, Office of Safety and Traffic Operations Research and Development, McLean, Va.

Civil Aeronautics Board planning study : STOL/VTOL air transportation systems /

Cover title.

Comparable systems analysis: evaluation of ten command centers as potential sites.

Turner-Fairbank Highway Research Center, McLean, Va.