Proceedings of the Third International Workshop on Neural Networks and Fuzzy Logic, volume 2

Papers presented at the Neural Networks and Fuzzy Logic Workshop sponsored by the National Aeronautics and Space Administration and cosponsored by the University of Houston, Clear Lake, held 1-3 Jun. 1992 at the Lyndon B. Johnson Space Center in Houston, Texas are included. During the three days approximately 50 papers were presented. Technical topics addressed included adaptive systems; learning algorithms; network architectures; vision; robotics; neurobiological connections; speech recognition and synthesis; fuzzy set theory and application, control and dynamics processing; space applications; fuzzy logic and neural network computers; approximate reasoning; and multiobject decision making

Proceedings of the Third International Workshop on Neural Networks and Fuzzy Logic, volume 1

Documented here are papers presented at the Neural Networks and Fuzzy Logic Workshop sponsored by the National Aeronautics and Space Administration and cosponsored by the University of Houston, Clear Lake. The workshop was held June 1-3, 1992 at the Lyndon B. Johnson Space Center in Houston, Texas. During the three days approximately 50 papers were presented. Technical topics addressed included adaptive systems; learning algorithms; network architectures; vision; robotics; neurobiological connections; speech recognition and synthesis; fuzzy set theory and application, control, and dynamics processing; space applications; fuzzy logic and neural network computers; approximate reasoning; and multiobject decision making

NASA Tech Briefs, February 1993

Topics include: Communication Technology; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery; Fabrication Technology; Mathematics and Information Sciences; Life Sciences

Modelling, real-time simulation and control of automotive windscreen wiper systems for electronic control unit development

In recent years there has been a growth in the automotive industry, coupled with a growth in the amount of electronic components and systems in a modern vehicle. The higher amount of electronics has led to an increased amount of Electronic Control Units (ECU) in a vehicle which require advanced simulation based testing procedures throughout their development process. One such method is Hardware in the Loop (HIL) simulation in which a real ECU is connected to simulation models of its environment via a real-time simulator. This project is concerned with developing a plant model of a windscreen wiper system for use in the development of Jaguar Land Rover’s (JLR) body electronics ECU. The system is divided into four parts which are modelled separately: Wiper motor, linkages, arm and blades, and the windscreen environment. The wiper motor and mechanical elements models are derived and implemented using the physical modelling tools SimScape and SimMechanics. A dynamic friction model describing the interaction between the wiper blades and the windscreen is developed, based on results presented in the literature. A simple aerodynamic model describing the forces on the wiper blades is also established. The parameters of the models are derived using three sequential optimisation methods: Transfer function parameter identification, Genetic Algorithms (GA) and a nonlinear least squares local optimiser. A transfer function relating the motor current to the voltage was derived for step one, and a bespoke GA has been developed for step two. The parameters were successfully identified. Following this, Artificial Neural Networks (ANN) were used to convert the physical models into real-time capable models suitable for HIL simulation. Finally, adaptive control systems are designed in order to maintain the motor at a constant velocity. The models are presented in a Simulink library and graphical user interface modelling tool for ease of use

NASA Space Engineering Research Center Symposium on VLSI Design

The NASA Space Engineering Research Center (SERC) is proud to offer, at its second symposium on VLSI design, presentations by an outstanding set of individuals from national laboratories and the electronics industry. These featured speakers share insights into next generation advances that will serve as a basis for future VLSI design. Questions of reliability in the space environment along with new directions in CAD and design are addressed by the featured speakers

Implementation of a friction estimation and compensation technique

This thesis reports implementation of a friction estimation and compensation technique on a special laboratory apparatus. In this work, experimental results are reported for the Coulomb friction observer. The Coulomb friction observer estimates the total friction present in a system, assuming it to be a constant function of velocity. An extension of the observer, utilizing a coupled velocity observer, is used when velocity is not measurable. A modification to the velocity observer is also implemented. Experimental results show a remarkable improvement in the friction estimates which are also compared to the actual friction measurements. The estimates are qualitatively similar to the actual friction, demonstrating the ability of the modified design to track a non-constant friction. Finally, extremely low velocities are experimentally obtained by using the friction compensation technique mentioned above, further proving that accurate control at low velocities is possible by friction estimation and compensation

Active neuro-fuzzy integrated vehicle dynamics controller to improve the vehicle handling adn stability at complicated maneuvers

With the recent advancements in vehicle’s industry, driving safety in passenger vehicles is considered one of the key issues in designing any vehicle. According to other studies Electronic Stability Control (ESC) is considered to be the greatest road safety innovation since the seatbelt. Yet ESC has its drawbacks, that encouraged the development of other stability systems to correct or compensate these draw backs. But to efficiently make up for the ESC problems the integration of various control systems is needed, which is a pretty complicated task on its own. Lately, solving this stability problem became a hot research topic accompanied by the market demands for improving the available stability systems. Therefore, this thesis aims to add an innovative approach to help improve the vehicle stability. This approach consists of an intelligent algorithm that collects data about the vehicle characteristics and behavior. Then it uses an Artificial Neural Network to construct a fuzzy logic control system through learning from the optimum control values that was generated beforehand by the intelligent algorithm. This way, the proposed controller didn’t depend only on experts’ knowledge like the other controllers presented in the literature. This makes the controller more generic and reliable which is a very important aspect in designing a safety critical controller, like the presented one, where any fault in it can lead to a fatal accident. Also using the technique of using an Artificial Neural Network to construct a fuzzy logic control allows benefiting from the learning and autoautoadaption capability of neural networks and the smooth controlling performance that fuzzy logic controllers offers. Simulations results show the effectiveness of the proposed controller for improving the vehicle stability in different driving maneuvers. Where the controller’s results were compared to an uncontrolled vehicle and another vehicle controlled by a controller from the literature. -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Cuando un vehículo entra en una curva a alta velocidad, la aceleración lateral producida hace que el vehículo tienda a ser más inestable y menos controlable desde el punto de vista del conductor. Esta inestabilidad, podría conllevar un comportamiento no deseado del vehículo, como el sub-viraje o el sobre-viraje, que pueden llevar al vehículo a salirse de su curso previsto o que vuelque. Además, las estadísticas concluyen que la inestabilidad lateral del vehículo es causa de accidentes de fatales consecuencias. Para hacer frente a este problema, se han propuesto varios sistemas de control, con el objetivo de generar una acción contraria que lleve de nuevo al vehículo a su curso deseado. Estos sistemas pretenden alterar de una manera u otra las fuerzas centrífugas del neumático con el fin de producir fuerzas de compensación que ayuden a mantener el control lateral del vehículo. Estos controladores presentan estrategias de control diferentes: algunos intentan afectar directamente a los ángulos de dirección de los neumáticos, otros inciden en las fuerzas longitudinales de los neumáticos para crear un momento de guiñaada alrededor del eje vertical del vehículo, y por último, otros intentan afectar a la distribución de la carga vertical entre los neumáticos. Por ello, debido a la diferencia de las características de cada uno de estos sistemas, sus capacidades de controlar también difieren. Sin desmerecer a ninguno de ellos, algunos demuestran mayor eficacia en situaciones de inestabilidad suaves; otros lo son cuando el vehículo llega a sus límites de adhesión, y los hay cuando la aceleración lateral supera un cierto valor. Por esta razón, se recomienda el uso de más de un sistema de control para beneficiarse de las ventajas de sus diferentes conceptos de control. Sin embargo, la combinación de más de un controlador de estabilidad de un vehículo, no es tarea fácil, dado que podrían producirse conflictos entre los diferentes controladores, así como la superposición de los diferentes objetivos de control. Adicionalmente, una simple combinación podría llevar a una mayor complejidad del hardware y el software usados, debido a la posible repetición de sensores y actuadores, y en consecuencia a una complejidad de cables de conexión. Por ello, se han propuesto sistemas de Dinámica de Vehículos de Control Integral (IVDC), para proporcionar una integración cuidadosamente diseñada con el objetivo de coordinar los diferentes sistemas de control del chasis. De esta manera, los conflictos de control podrían ser eliminados, y los resultados podrían reforzarse aún más mediante tal combinación. Igualmente el coste y la complejidad del sistema podrían reducirse debido al posible uso compartido de sensores, actuadores, unidades de control y cables. Recientemente, los sistemas de IVDC han sido un tema de investigación recurrente, existiendo distintos sistemas en la literatura que han intentado controlar varias combinaciones de los citados controladores utilizando una variedad de técnicas de control, muchos de los cuales han mostrado resultados prometedores en la mejora del manejo del vehículo a través de los resultados de simulaciones. No obstante, estos sistemas eran manualmente diseñados y probados en un número limitado de maniobras y condiciones. Además, han sido testados en las mismas maniobras utilizadas para su dise˜no y, por tanto, su fiabilidad y previsibilidad son cuestionables. Por otra parte, los sistemas de control de estabilidad del vehículo son considerados como sistemas de seguridad crítica, donde cualquier error podría causar un accidente fatal. De este modo, como consecuencia de la imprecisión humana, un controlador diseñado manualmente que ha sido desarrollado a través de pruebas de situación limitada, es propenso a errores que generan deficiencias en ciertas zonas de control o a inexactitudes en las decisiones de los valores de control. Por otra parte, la selección manual del margen de control dedicado a cada sub-sistema integrado no asegura la optimización de las capacidades de los controladores. Además, dado que estos controladores son diseñados por el hombre, cualquier variación de las características del modelo del vehículo, como por ejemplo algo tan sencillo como el cambio en la rigidez de la suspensión, necesitaría de intervención humana para volver a calibrar o volver a ajustar manualmente el sistema con el objetivo de adaptarse a la variación realizada. Por lo tanto, en esta tesis se intentará reemplazar el conocimiento humano y los sistemas diseñados manualmente, por un sistema automatizado e inteligente, que autoconstruye el sistema de control sin intervención humana. Este método utilizará una red neuronal inteligente que aprende los valores óptimos de control a través de un algoritmo extenso de minería de datos. En consecuencia, se autoconstruye un controlador de lógica difusa que corrige la estabilidad del vehículo a través de un sistema activo de corrección de la entrada al volante y un sistema de control de ángulo de guiñada mediante los frenos. Las entradas de control de estos sistemas serán la velocidad del ángulo de guiñada y el ángulo de deslizamiento lateral, siendo los controladores más eficaces presentados en la literatura

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field