The achievement of spacecraft autonomy through the thematic application of multiple cooperating intelligent agents

A description is given of UNICORN, a prototype system developed for the purpose of investigating artificial intelligence (AI) concepts supporting spacecraft autonomy. UNICORN employs thematic reasoning, of the type first described by Rodger Schank of Northwestern University, to allow the context-sensitive control of multiple intelligent agents within a blackboard based environment. In its domain of application, UNICORN demonstrates the ability to reason teleologically with focused knowledge. Also presented are some of the lessons learned as a result of this effort. These lessons apply to any effort wherein system level autonomy is the objective

Sliding Mode Control for Trajectory Tracking of a Non-holonomic Mobile Robot using Adaptive Neural Networks

In this work a sliding mode control method for a non-holonomic mobile robot using an adaptive neural network is proposed. Due to this property and restricted mobility, the trajectory tracking of this system has been one of the research topics for the last ten years. The proposed control structure combines a feedback linearization model, based on a nominal kinematic model, and a practical design that combines an indirect neural adaptation technique with sliding mode control to compensate for the dynamics of the robot. A neural sliding mode controller is used to approximate the equivalent control in the neighbourhood of the sliding manifold, using an online adaptation scheme. A sliding control is appended to ensure that the neural sliding mode control can achieve a stable closed-loop system for the trajectory-tracking control of a mobile robot with unknown non-linear dynamics. Also, the proposed control technique can reduce the steady-state error using the online adaptive neural network with sliding mode control; the design is based on Lyapunov’s theory. Experimental results show that the proposed method is effective in controlling mobile robots with large dynamic uncertaintiesFil: Rossomando, Francisco Guido. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Soria, Carlos Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Carelli Albarracin, Ricardo Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentin

Discrete neural compensator algorithm of dynamic in mobile robots using extended Kalman filter

Este artículo presenta el diseño de un algoritmo basado en redes neuronales en tiempo discreto para su aplicación en robótica móvil. También se muestran las condiciones de estabilidad y una evaluación de los resultados. El robot móvil en el cual se aplicó el algoritmo neural posee 2 controladores en cascada, uno para la cinemática y otro para la dinámica; ambos controladores están basados en la linealización por realimentación. El controlador de la dinámica solo posee la información de la dinámica nominal (parámetros). La red neuronal de compensación se adapta para reducir las perturbaciones ocasionadas por las variaciones en la dinámica y las incertidumbres existentes en el modelo, y esas diferencias en la dinámica entre el modelo nominal y el real son aprendidas por una red neuronal RBF (funciones de base radial) usando el filtro de Kalman extendido para el ajuste de los pesos de salida de las funciones de base radial. El algoritmo de compensación neuronal es eficiente, ya que el costo computacional es menor que el necesario para aprender la totalidad de la dinámica y al mismo tiempo posee la robustez que podría aprender la totalidad de la dinámica en caso de fallo del controlador dinámico. En este trabajo se muestra un análisis de estabilidad del algoritmo neuronal adaptable, y además se comprueba que los errores de control están acotados en función del error de aproximación de la red neuronal RBF. Se muestran resultados de experimentación sobre un robot móvil que prueban la viabilidad práctica y el rendimiento para el control de los mismos.This paper presents the design of an algorithm based on neural networks in discrete time for its application in mobile robots. In addition, the system stability is analyzed and an evaluation of the experimental results is shown. The mobile robot has two controllers, one addressed for the kinematics and the other one designed for the dynamics. Both controllers are based on the feedback linearization. The controller of the dynamics only has information of the nominal dynamics (parameters). The neural algorithm of compensation adapts its behaviour to reduce the perturbations caused by the variations in the dynamics and the model uncertainties. Thus, the differences in the dynamics between the nominal model and the real one are learned by a neural network RBF (radial basis functions) where the output weights are set using the extended Kalman filter. The neural compensation algorithm is efficient, since the consumed processing time is lower than the one required to learning the totality of the dynamics. In addition, the proposed algorithm is robust with respect to failures of the dynamic controller. In this work, a stability analysis of the adaptable neural algorithm is shown and it is demonstrated that the control errors are bounded depending on the error of approximation of the neural network RBF. Finally, the results of experiments performed by using a mobile robot are shown to test the viability in practice and the performance for the control of robots.Peer Reviewe

analysis of the impact of diesel ethanol fuel blends on ci engine performance and emissions via multi zone combustion modelling

Abstract Nowadays the high competition reached in the automotive market forces original equipment manufacturers (OEMs) towards the implementation of more and more innovative solutions. Strict emission standards and fuel economy targets make the work hard to be accomplished. Therefore modern engines feature complex architecture and embed new devices for exhaust gas recirculation (LP-HP EGR), turbocharging (e.g. multi-stage compressors), gas after-treatment (e.g. DPF, SCR, LNT) and fuel injection. This results in increased costs for engine and components as well as great complexity for the overall powertrain management. An alternative solution to comply with emissions and CO 2 standards is to supply the engine with alternative fuel blends that allow reducing significantly engine pollutants thus lowering the complexity of the after-treatment path. The paper deals with the analysis of the impact of different fuel blends Diesel-Ethanol on the performance and NOx / Particulate emissions in a common-rail CI engine. The simulation analyses are performed by a multi-zone phenomenological model of fuel spray, combustion and emissions mechanisms, that takes into account the influence of the specific fuel blend on the fuel-air mixture formation and the in-cylinder gas mixture evolution. Model validation is carried out vs. experimental data collected on an automotive common-rail CI engine, fuelled by a E20 blend and operating at different working conditions. Afterwards simulations are performed by spanning the fuel blends and the combustion control parameters (i.e. injection pattern and EGR) with the aim of optimizing combustion tuning vs. fuel blend

Adaptive neural dynamic compensator for mobile robots in trajectory tracking control

In the present paper, it will be reported original results concerning the application of Neural Networks (NN) in mobile robot in trajectory tracking control. This work combines a feedback linearization based on a nominal model and an NN adaptive dynamic compensation. In mobile robot with uncertain dynamic parameters, two controllers are implemented separately: a kinematic controller and an inverse dynamic controller. The uncertainty in the nominal dynamic model is compensated by a neural adaptive feedback controller. The resulting adaptive controller is efficient and robust in the sense that it succeeds to achieve a good tracking performance with a small computational effort. The learning laws were deduced by Lyapunovs stability analysis. Finally, the performance of the control system is verified through experiments.Fil: Rossomando, Francisco Guido. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Soria, Carlos Miguel. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Carelli Albarracin, Ricardo Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentin

Do Open Access Dental Articles Enjoy Higher Altmetric Attention Scores, Twitter, Facebook, News, Wikipedia, Blog mentions, Mendeley Readers and Citations?

In order to access articles published in conventional (non-open access) journals, scientists must utilize tools such as subscriptions, site licenses or pay-per-view charges. In contrast, open access articles can be accessed without financial, legal or technical barriers. A large-scale study estimated that at least 28% of the academic literature is open access (19 million in total) and that this percentage is growing.[1] A recent survey showed an open access rate in field of dentistry at 45.8%.[2] It has been demonstrated that open access articles had 8% to 40% higher citations compared to non-open access articles; this has been termed, “open access citation advantage”.[3],[4],[5

Neural network-based compensation control of mobile robots with partially known structure

This study proposes an inverse non-linear controller combined with an adaptive neural network proportional integral (PI) sliding mode using an on-line learning algorithm. The neural network acts as a compensator for a conventional inverse controller in order to improve the control performance when the system is affected by variations on their dynamics and kinematics. Also, the proposed controller can reduce the steady-state error of a non-linear inverse controller using the on-line adaptive technique based on Lyapunov’s theory. Experimental results show that the proposed method is effective in controlling dynamic systems with unexpected large uncertainties.Fil: Rossomando, Francisco Guido. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Soria, Carlos Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Carelli Albarracin, Ricardo Oscar. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Estabelecimento de Estrategias de Controle Inteligente na Lamina ao de produtosPlanos

Este trabalho de tese propõe e explicita uma estratégia de controle neural para o processo de controle de variação da espessura da tira num trem de laminação a quente. A qualidade do produto laminado depende da minimização da variação da espessura da tira e da coroa da mesma. O modelo do sistema, extremamente complexo, é apresentado numa formulação matemática e serve de base para um ambiente de simulação, desenvolvido para apoiar a validação das estratégias proposta, que também pode ser utilizado no desenvolvimento de outras estratégias. A estratégia proposta apresenta um melhor desempenho quando comparada com os resultados reais do controle convencional de um trem de laminação, gentilmente fornecidos pela SIDERAR S. A., siderúrgica da Argentina