Water Quality Monitoring and Associated Distributed Measurement Systems: An Overview

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Lifelong topological visual navigation

La possibilité pour un robot de naviguer en utilisant uniquement la vision est attrayante en raison de sa simplicité. Les approches de navigation traditionnelles basées sur la vision nécessitent une étape préalable de construction de carte qui est ardue et sujette à l'échec, ou ne peuvent que suivre exactement des trajectoires précédemment exécutées. Les nouvelles techniques de navigation visuelle basées sur l'apprentissage réduisent la dépendance à l'égard d'une carte et apprennent plutôt directement des politiques de navigation à partir des images. Il existe actuellement deux paradigmes dominants : les approches de bout en bout qui renoncent entièrement à la représentation explicite de la carte, et les approches topologiques qui préservent toujours une certaine connectivité de l'espace. Cependant, alors que les méthodes de bout en bout ont tendance à éprouver des difficultés dans les tâches de navigation sur de longues distances, les solutions basées sur les cartes topologiques sont sujettes à des défaillances dues à des arêtes erronées dans le graphe. Dans ce document, nous proposons une méthode de navigation visuelle topologique basée sur l'apprentissage, avec des stratégies de mise à jour du graphe, qui améliore les performances de navigation sur toute la durée de vie du robot. Nous nous inspirons des algorithmes de planification basés sur l'échantillonnage pour construire des graphes topologiques basés sur l'image, ce qui permet d'obtenir des graphes plus épars et d'améliorer les performances de navigation par rapport aux méthodes de base. En outre, contrairement aux contrôleurs qui apprennent à partir d'environnements d'entraînement fixes, nous montrons que notre modèle peut être affiné à l'aide d'un ensemble de données relativement petit provenant de l'environnement réel où le robot est déployé. Enfin, nous démontrons la forte performance du système dans des expériences de navigation de robots dans le monde réel.The ability for a robot to navigate using vision only is appealing due to its simplicity. Traditional vision-based navigation approaches require a prior map-building step that was arduous and prone to failure, or could only exactly follow previously executed trajectories. Newer learning-based visual navigation techniques reduce the reliance on a map and instead directly learn policies from image inputs for navigation. There are currently two prevalent paradigms: end-to-end approaches forego the explicit map representation entirely, and topological approaches which still preserve some loose connectivity of the space. However, while end-to-end methods tend to struggle in long-distance navigation tasks, topological map-based solutions are prone to failure due to spurious edges in the graph. In this work, we propose a learning-based topological visual navigation method with graph update strategies that improves lifelong navigation performance over time. We take inspiration from sampling-based planning algorithms to build image-based topological graphs, resulting in sparser graphs with higher navigation performance compared to baseline methods. Also, unlike controllers that learn from fixed training environments, we show that our model can be finetuned using a relatively small dataset from the real-world environment where the robot is deployed. Finally, we demonstrate strong system performance in real world robot navigation experiments

Jahresbericht Forschung und Entwicklung 2004

Forschungsjahresbericht 2004 der Fachhochschule Konstan

Modelização cinzenta aplicada à síntese de controladores com baixa complexidade

Doutoramento em Engenharia ElectrotécnicaNas últimas décadas tem-se assistido à automatização generalizada da maioria dos sistemas e equipamentos que nos rodeiam no dia-a-dia, desde os sistemas industriais, de transporte, até aos pequenos electrodomésticos. A automatização dos sistemas torna-os mais inteligentes, no sentido de maior capacidade de adaptação operacional e maior eficácia, facilitando e simplificando a sua utilização. O problema que serviu de motivação ao desenvolvimento deste trabalho foi precisamente a automatização de um equipamento de aquecimento de água a gás, conhecido por esquentador doméstico. Neste sentido, o presente trabalho propõe novas estratégias de automatização inteligentes para o controlo de um esquentador, adaptadas à execução em sistemas embutidos de baixo poder de cálculo. Este trabalho suporta a tese de que a utilização de modelos com um elevado nível de interpretabilidade propicia a construção de estruturas com baixa complexidade matemática, potenciando a simplicidade das malhas de controlo. A interpretabilidade dos modelos deste tipo, tipicamente classificados como de caixa cinzenta ou cinzentos, depende do tipo de conhecimentos utilizados e incorporados na sua construção. Existindo, desde modelos construídos e identificados com base em dados de entrada e saída que apresentam baixos níveis de interpretabilidade, passando por modelos baseados em conhecimentos experimentais com níveis de interpretabilidade intermédios até modelos baseados em conhecimentos fenomenológicos que apresentam elevados níveis de interpretabilidade. Com o objectivo de construir modelos para o esquentador com um razoável nível de interpretabilidade são neste trabalho apresentados e explorados três métodos de modelização neuro-difusa (modelização cinzenta escura) orientada por blocos (modelização cinzenta) e híbrida (modelização cinzenta clara). Neste âmbito, são propostos como modelos para o esquentador: o modelo neuro-difuso, Hammerstein adaptativo polinomial/neuro-difuso e híbrido série polinomial/neuro-difuso. Utilizando os referidos modelos são propostas três malhas de controlo baseadas no controlador Smith preditivo, com algumas simplificações que resultam da interpretabilidade dos respectivos modelos, tal como a linearização do esquentador relativamente ao fluxo de gás. Finalmente, os desempenhos dos vários controladores assim como os respectivos níveis de interpretabilidade e de complexidade matemática são comparados. Este trabalho apresenta diversas contribuições ao nível das diferentes abordagens de modelização e da definição de algoritmos de controlo a aplicar num esquentador doméstico: · Definição e identificação do modelo neuro-difuso, dos modelos Hammerstein adaptativo polinomial/neuro-difuso e dos modelos híbridos série polinomial/neuro-difuso, que exploram as várias metodologias de combinação de conhecimentos a priori na sua construção (modelização cinzenta); · Proposta de definição da equação de balanço de energias de um esquentador doméstico; · Proposta de alteração da malha de controlo Smith preditivo de forma a linearizar o sistema relativamente a uma dada variável de entrada; · Apresentação da malha de controlo Smith preditivo para sistemas com múltiplas entradas com diferentes tempos mortos; · Aplicação dos vários modelos cinzentos no controlo automático de um esquentador doméstico, obtendo controladores adaptativos e não adaptativos de complexidade matemática relativamente baixa. Este trabalho disponibiliza uma gama diversificada de soluções de controlo automático para o esquentador com semelhantes níveis de desempenho, mas com diferentes níveis de interpretabilidade e de complexidade matemática. De referir que os testes e as avaliações das soluções de controlo apresentadas, além de simuladas antecipadamente num computador, foram efectuados com dados reais

Fault Detection, Diagnosis and Fault Tolerance Approaches in Dynamic Systems based on Black-Box Models

In this dissertation new contributions to the research area of fault detection and diagnosis in dynamic systems are presented. The main research effort has been done on the development of new on-line model-based fault detection and diagnosis (FDD) approaches based on blackbox models (linear ARX models, and neural nonlinear ARX models). From a theoretical point of view a white-box model is more desirable to perform the FDD tasks, but in most cases it is very hard, or even impossible, to obtain. When the systems are complex, or difficult to model, modelling based on black-box models is usually a good and often the only alternative. The performance of the system identification methods plays a crucial role in the FDD methods proposed. Great research efforts have been made on the development of linear and nonlinear FDD approaches to detect and diagnose multiplicative (parametric) faults, since most of the past research work has been done focused on additive faults on sensors and actuators. The main pre-requisites for the FDD methods developed are: a) the on-line application in a real-time environment for systems under closed-loop control; b) the algorithms must be implemented in discrete time, and the plants are systems in continuous time; c) a two or three dimensional space for visualization and interpretation of the fault symptoms. An engineering and pragmatic view of FDD approaches has been followed, and some new theoretical contributions are presented in this dissertation. The fault tolerance problem and the fault tolerant control (FTC) have been investigated, and some ideas of the new FDD approaches have been incorporated in the FTC context. One of the main ideas underlying the research done in this work is to detect and diagnose faults occurring in continuous time systems via the analysis of the effect on the parameters of the discrete time black-box ARX models or associated features. In the FDD methods proposed, models for nominal operation and models for each faulty situation are constructed in off-line operation, and used a posteriori in on-line operation. The state of the art and some background concepts used for the research come from many scientific areas. The main concepts related to data mining, multivariate statistics (principal component analysis, PCA), linear and nonlinear dynamic systems, black-box models, system identification, fault detection and diagnosis (FDD), pattern recognition and discriminant analysis, and fault tolerant control (FTC), are briefly described. A sliding window version of the principal components regression algorithm, termed SW-PCR, is proposed for parameter estimation. The sliding window parameter estimation algorithms are most appropriate for fault detection and diagnosis than the recursive algorithms. For linear SISO systems, a new fault detection and diagnosis approach based on dynamic features (static gain and bandwidth) of ARX models is proposed, using a pattern classification approach based on neural nonlinear discriminant analysis (NNLDA). A new approach for fault detection (FDE) is proposed based on the application of the PCA method to the parameter space of ARX models; this allows a dimensional reduction, and the definition of thresholds based on multivariate statistics. This FDE method has been combined with a fault diagnosis (FDG) method based on an influence matrix (IMX). This combined FDD method (PCA & IMX) is suitable to deal with SISO or MIMO linear systems. Most of the research on the fault detection and diagnosis area has been done for linear systems. Few investigations exist in the FDD approaches for nonlinear systems. In this work, two new nonlinear approaches to FDD are proposed that are appropriate to SISO or MISO systems. A new architecture for a neural recurrent output predictor (NROP) is proposed, incorporating an embedded neural parallel model, an external feedback and an adjustable gain (design parameter). A new fault detection and diagnosis (FDD) approach for nonlinear systems is proposed based on a bank of neural recurrent output predictors (NROPs). Each neural NROP predictor is tuned to a specific fault. Also, a new FDD method based on the application of neural nonlinear PCA to ARX model parameters is proposed, combined with a pattern classification approach based on neural nonlinear discriminant analysis. In order to evaluate the performance of the proposed FDD methodologies, many experiments have been done using simulation models and a real setup. All the algorithms have been developed in discrete time, except the process models. The process models considered for the validation and tests of the FDD approaches are: a) a first order linear SISO system; b) a second order SISO model of a DC motor; c) a MIMO system model, the three-tank benchmark. A real nonlinear DC motor setup has also been used. A fault tolerant control (FTC) approach has been proposed to solve the typical reconfiguration problem formulated for the three-tank benchmark. This FTC approach incorporates the FDD method based on a bank of NROP predictors, and on an adaptive optimal linear quadratic Gaussian controller

A Study of Predictive Control Strategies for Optimally Designed Solar Homes

This thesis investigates the development of predictive control strategies for optimally or near-optimally designed solar homes. Optimal design refers to the integration of renewable energy technologies (mainly active and passive solar) with a high-quality building envelope as well as efficiency and conservation measures to achieve substantial reductions in energy consumption and peak demand. Effective implementation of these technologies requires an integrated design approach, which considers their interactions with the building and its services. Furthermore, control strategies must be an essential part of the integrated design of a building to improve energy performance and ensure occupant comfort. In optimally designed solar homes, control strategies should incorporate the collection, storage and delivery of solar energy. Weather forecasts along with an understanding of the building’s thermal dynamics (e.g., time delays due to thermal mass) enable predicting and managing loads and solar energy availability. Design and operation strategies of a case study, the Alstonvale House, are presented. Features of this house include passive solar design, a building-integrated photovoltaic/thermal (BIPV/T) system coupled with a solar-assisted heat pump, a thermal energy storage tank and a radiant floor heating system in a thermally massive concrete slab. Design and control approaches developed for the Alstonvale House provided the basis for generalized control strategies applicable to optimally designed solar homes. Simplified building models, which can be derived from more detailed models or on-site measurements, can facilitate the implementation of predictive control techniques. In this investigation, model-based predictive control was applied to a radiant floor heating system and the position of roller blinds in a room with high solar gains. Predictive control can also be applied to optimize the operation of renewable energy systems. In this study, forecasts of heating loads and solar radiation were used in a dynamic programming algorithm to select a near-optimal set-point trajectory for an energy storage tank heated with a heat pump assisted by a BIPV/T system

Flachheitsbasierte Methode zum stoßfreien Umschalten von Reglerstrukturen

Die vorliegende Arbeit beschäftigt sich mit der Frage, wie stoßfreie Rekonfigurationen von Systemen zur Laufzeit realisiert werden können. Es werden Anforderungen an die Rekonfiguration definiert und eine neue Methode zur stoßfreien Rekonfiguration vorgestellt, die sowohl bei einfachen Betriebspunktwechseln als auch beim Wechsel der Reglerparameter oder der Reglerstruktur angewendet werden kann. Die Methodik basiert auf der Zwei-Freiheitsgrade-Reglerstruktur und der (differenziellen) Flachheit, einer grundlegenden Eigenschaft des Systems selbst. Die Methodik wird für lineare und nichtlineare Ein- und Mehrgrößensysteme vorgestellt, wobei die Rekonfigurationen immer mittels in Echtzeit berechneter Vorsteuerungs- und Führungsgrößentrajektorien realisiert werden. Anhand von akademischen und praktischen Beispielen wird die neue Methode mit bestehenden Verfahren zur stoßfreien Reglerumschaltung verglichen und die Anwendbarkeit demonstriert.The present thesis deals with a new approach to bumpless transfer for system reconfiguration at runtime. During a system reconfiguration an operating point change, a change of controller parameters or even a change of the control structure can occur. After the definition of requirements which has to be fulfilled during the reconfiguration, a new flatness-based method for bumpless transfer is presented. The flatness-based method draws on the two-degrees-of-freedom control structure and on the (differential) flatness which is a fundamental feature of the controlled system. Bumpless switching is realised by means of feedforward and reference trajectories computed in real time which are applicable with linear and non-linear SISO and MIMO systems. The new method of bumpless switching is compared to existing bumpless-switching procedures and its advantages are evidenced by practical examples.Tag der Verteidigung: 11.12.2014Paderborn, Univ., Diss., 201

Desarrollo y validación de un modelo dinámico para una pila de combustible tipo PEM

JORNADAS DE AUTOMÁTICA (27) (27.2006.ALMERÍA)El objetivo de este trabajo es realizar un modelo dinámico detallado de una pila de combustible tipo PEM de 1.2 kW de potencia nominal. El modelo desarrollado incluye efectos como el ’flooding’ y la dinámica de la temperatura y es de utilidad para poder diseñar y ensayar controles tanto de la válvula de purga como de la refrigeración de la pila mediante un ventilador. Se ha desarrollado un novedoso tratamiento de la ecuación experimental que modela la curva de polarización que simplifica considerablemente su caracterización. Por último el modelo realizado ha sido validado con datos tomados de una pila real