An unknown input observer provides perfect asymptotic tracking of the state of a system affected by unknown inputs. Such an observer exists (possibly requiring a delay in estimation) if and only if the system satisfies a property known as strong detectability. In this thesis, we consider the problem of selecting (at design-time) a minimum cost subset of sensors from a given set to make a given system strongly detectable. We show this problem is NP-hard even when the system is stable. Furthermore, we show it is not possible to approximate the minimum cost within a factor of log(n) in polynomial-time (unless P=NP). However, we prove if a given system (with a selected set of sensors) is already strongly detectable, finding the smallest set of additional sensors to install to obtain a zero-delay observer can be done in polynomial time. Next we consider the problem of attacking a set of deployed sensors to remove the property of strong detectability. We show finding the smallest number of sensors to remove is NP-hard. Lastly through simulations, we analyze two greedy approaches for approximating the strong detectability sensor selection problem.

We present new adaptive control algorithms that address the problem of rejecting sinusoids with known frequencies that act on an unknown asymptotically stable linear time-invariant system. To achieve asymptotic disturbance rejection, adaptive control algorithms of this dissertation rely on limited or no system model information. These algorithms are developed in discrete time, meaning that the control computations use sampled-data measurements. We demonstrate the effectiveness of algorithms via analysis, numerical simulations, and experimental testings. We also present extensions to these algorithms that address systems with decentralized control architecture and systems subject to disturbances with unknown frequencies.

Esta dissertação tem a intenção de apresentar uma sequência didática que foi aplicada para um grupo de alunos do Ensino Fundamental II com o objetivo de potencializar sua compreensão sobre a praticidade e conveniência da linguagem algébrica. A sequência didática é composta por atividades lúdicas e atividades algébricas que buscaram estimular que os alunos produzam notações e símbolos de forma autônoma, que fizesse sentido para eles, bem como instigar sua curiosidade, em um ambiente um ambiente que estimule a pensar e resolver problemas, onde a relação professor - aluno tenha como base a confiança e o respeito mútuo. O desenvolvimento da pesquisa foi feito por meio de um estudo de caso. As atividades foram aplicadas para uma turma de 6o ano com a intenção de produzir significado para o conceito de incógnita. A análise da efetividade da sequência didática levou em consideração os aspectos particulares dos alunos e da escola, o campo semântico no qual os alunos trabalharam e os tipos de equação do primeiro grau com uma incógnita que os alunos conseguiram resolver. A pesquisa nos permitiu concluir que o trabalho inicial de motivação e entendimento da linguagem algébrica facilita a aceitação, pelos alunos, do uso da letra no lugar de um número desconhecido, tornando-se um facilitador para o aprendizado de equações do primeiro grau. / This dissertation intends to present a didactic sequence that was applied to a group of students from Ensino Fundamental II (6th to 9th grades) in order to enhance their understanding of practicality and convenience of the algebraic language. The didactic sequence consists of recreational activities and algebraic activities, which seeks to stimulate the students to produce notations and symbols autonomously, in a way that makes sense for them, instigating their curiosity, in an environment that encourages to think and solve problems, where the teacher-student relation is based after trustfulness and mutual respect. The development of this research was made through a case study. The activities were applied to a 6th grade class, in order to produce meaning to the concept of the unknown variable. The analysis of the effectiveness of the didactic sequence took into account the particular aspects of the students and the school, the semantic field in which they have worked and kinds of equations with one unknown variable they managed to solve. The research allowed us to conclude that the initial work of motivating and understanding of the algebraic language facilitates the acceptance of the use of a letter instead of an unknown number, becoming a facilitator for learning first degree equations.

<p>The subject of this thesis is the design of a control law for a magnetic levitation system, which in this case is the system 33-210. The method used is backstepping technique and specifically adaptive observer backstepping due to parameter uncertainties and lack of access to all the states of the system. The second state of the system, the speed of the steel ball, was estimated by a reduced order observer. The model used gave us the opportunity to estimate a parameter which in the literature is denoted virtual control coefficient. Backstepping method gives us a rather straight forward way to design the controlling unit for a system with these properties. Stabilization of the closed-loop system is achieved by incorporating a Lypapunov function, which were chose a quadratic one in this thesis. If thederivative of this function is rendered negative definite by the control law, then we achieve stability. The results of the design were evaluated in simulations and real-time measurements by testing the tracking performance of the system. The simulation results were very promising and the validations in real-time were satisfying. Note that this has been done in previous studies; the new aspect here is the limitation of the voltage input. The real-time results showed that the parameter estimation converges only locally.</p>

In this thesis I aim to improve phrase-based statistical machine translation (PBSMT) in a number of ways by the use of text harmonization strategies. PBSMT systems are built by training statistical models on large corpora of human translations. This architecture generally performs well for languages with similar structure. If the languages are different for example with respect to word order or morphological complexity, however, the standard methods do not tend to work well. I address this problem through text harmonization, by making texts more similar before training and applying a PBSMT system. I investigate how text harmonization can be used to improve PBSMT with a focus on four areas: compounding, definiteness, word order, and unknown words. For the first three areas, the focus is on linguistic differences between languages, which I address by applying transformation rules, using either rule-based or machine learning-based techniques, to the source or target data. For the last area, unknown words, I harmonize the translation input to the training data by replacing unknown words with known alternatives. I show that translation into languages with closed compounds can be improved by splitting and merging compounds. I develop new merging algorithms that outperform previously suggested algorithms and show how part-of-speech tags can be used to improve the order of compound parts. Scandinavian definite noun phrases are identified as a problem forPBSMT in translation into Scandinavian languages and I propose a preprocessing approach that addresses this problem and gives large improvements over a baseline. Several previous proposals for how to handle differences in reordering exist; I propose two types of extensions, iterating reordering and word alignment and using automatically induced word classes, which allow these methods to be used for less-resourced languages. Finally I identify several ways of replacing unknown words in the translation input, most notably a spell checking-inspired algorithm, which can be trained using character-based PBSMT techniques. Overall I present several approaches for extending PBSMT by the use of pre- and postprocessing techniques for text harmonization, and show experimentally that these methods work. Text harmonization methods are an efficient way to improve statistical machine translation within the phrase-based approach, without resorting to more complex models.

Searching for objects in physical space is one of the most important tasks for humans. Mobile sensor networks can be great tools for the task. Transient targets refer to a class of objects which are not identifiable unless momentary sensing and signaling conditions are satisfied. The transient property is often introduced by target attributes, privacy concerns, environment constraints, and sensing limitations. Transient target localization problems are challenging because the transient property is often coupled with factors such as sensing range limits, various coverage functions, constrained mobility, signal correspondence, limited number of searchers, and a vast searching region. To tackle these challenge tasks, we gradually increase complexity of the transient target localization problem such as Single Robot Single Target (SRST), Multiple Robots Single Target (MRST), Single Robot Multiple Targets (SRMT) and Multiple Robots Multiple Targets (MRMT). We propose the expected searching time (EST) as a primary metric to assess the searching ability of a single robot and the spatiotemporal probability occupancy grid (SPOG) method that captures transient characteristics of multiple targets and tracks the spatiotemporal posterior probability distribution of the target transmissions. Besides, we introduce a team of multiple robots and develop a sensor fusion model using the signal strength ratio from the paired robots in centralized and decentralized manners. We have implemented and validated the algorithms under a hardware-driven simulation and physical experiments.

Economic systems are often subject to structural variability. For the achievement of correct structural specification in econometric modelling it is then important to allow for parameters that are time-varying, and to apply estimation techniques suitably designed for inference in such models. One realistic model assumption for such parameter variability is the Markovian model, and Kaiman filtering is then assumed to be a convenient estimator. In the thesis several aspects of using Kaiman filtering approaches to estimation in that framework are considered. The application of the Kaiman filter to estimation in econometric models is straightforward if a set of basic assumptions are satisfied, and if necessary initial specifications can be accurately made. Typically, however, these requirements can generally not be perfectly met. It is therefore of great importance to know the consequences of deviations from the basic assumptions and correct initial specifications for inference, in particular for the small sample situations typical in econometrics. If the consequences are severe it is essential to develop techniques to cope with such aspects.For estimation in interdependent systems a two stage Kaiman filter is proposed and evaluated, theoretically, as well as by a small sample Monte Carlo study, and empirically. The estimator is approximative, but with promising small sample properties. Only if the transition matrix of the parameter model and an initial parameter vector are misspecified, the performance deteriorates. Furthermore, the approach provides useful information about structural properties, and forms a basis for good short term forecasting.In a reduced form fraaework most of the basic assumptions of the traditional Kaiman filter are relaxed, and the implications are studied. The case of stochastic regressors is, under reasonable additional assumptions, shown to result in an estimator structurally similar to that due to the basic assumptions. The robustness properties are such that in particular the transition matrix and the initial parameter vector should be carefully estimated. An estimator for the joint estimation of the transition matrix, the parameter vector and the model residual variance is suggested and utilized to study the consequences of a misspecified parameter model. By estimating th transitions the parameter estimates are seen to be robust in this respect. / <p>Härtill 4 delar</p> / digitalisering@umu

Navigation in mobile robotics involves two tasks, keeping track of the robot's position and moving according to a control strategy. In addition, when no prior knowledge of the environment is available, the problem is even more difficult, as the robot has to build a map of its surroundings as it moves. These three problems ought to be solved in conjunction since they depend on each other. This thesis is about simultaneously controlling an autonomous vehicle, estimating its location and building the map of the environment. The main objective is to analyse the problem from a control theoretical perspective based on the EKF-SLAM implementation. The contribution of this thesis is the analysis of system's properties such as observability, controllability and stability, which allow us to propose an appropriate navigation scheme that produces well-behaved estimators, controllers, and consequently, the system as a whole. We present a steady state analysis of the SLAM problem, identifying the conditions that lead to partial observability. It is shown that the effects of partial observability appear even in the ideal linear Gaussian case. This indicates that linearisation alone is not the only cause of SLAM inconsistency, and that observability must be achieved as a prerequisite to tackling the effects of linearisation. Additionally, full observability is also shown to be necessary during diagonalisation of the covariance matrix, an approach often used to reduce the computational complexity of the SLAM algorithm, and which leads to full controllability as we show in this work.Focusing specifically on the case of a system with a single monocular camera, we present an observability analysis using the nullspace basis of the stripped observability matrix. The aim is to get a better understanding of the well known intuitive behaviour of this type of systems, such as the need for triangulation to features from different positions in order to get accurate relative pose estimates between vehicle and camera. Through characterisation the unobservable directions in monocular SLAM, we are able to identify the vehicle motions required to maximise the number of observable states in the system. When closing the control loop of the SLAM system, both the feedback controller and the estimator are shown to be asymptotically stable. Furthermore, we show that the tracking error does not influence the estimation performance of a fully observable system and viceversa, that control is not affected by the estimation. Because of this, a higher level motion strategy is required in order to enhance estimation, specially needed while performing SLAM with a single camera. Considering a real-time application, we propose a control strategy to optimise both the localisation of the vehicle and the feature map by computing the most appropriate control actions or movements. The actions are chosen in order to maximise an information theoretic metric. Simulations and real-time experiments are performed to demonstrate the feasibility of the proposed control strategy.

The subject of this thesis is the design of a control law for a magnetic levitation system, which in this case is the system 33-210. The method used is backstepping technique and specifically adaptive observer backstepping due to parameter uncertainties and lack of access to all the states of the system. The second state of the system, the speed of the steel ball, was estimated by a reduced order observer. The model used gave us the opportunity to estimate a parameter which in the literature is denoted virtual control coefficient. Backstepping method gives us a rather straight forward way to design the controlling unit for a system with these properties. Stabilization of the closed-loop system is achieved by incorporating a Lypapunov function, which were chose a quadratic one in this thesis. If thederivative of this function is rendered negative definite by the control law, then we achieve stability. The results of the design were evaluated in simulations and real-time measurements by testing the tracking performance of the system. The simulation results were very promising and the validations in real-time were satisfying. Note that this has been done in previous studies; the new aspect here is the limitation of the voltage input. The real-time results showed that the parameter estimation converges only locally.

This thesis proposes that an investigation into the phenomenon of doubling may engage architecture with a type of desire that has deep rooted connections with the complexities of human nature, with the very human condition of desiring to know who/what/where/when/how we are. It proposes that an experience of doubling is suggestive of a specific kind of affective space that tests this relationship, expanding into the interval we have formed between our body, its being and space. The proposal is to explore the material, spatial, and psychological characteristics of such a phenomenon - to understand the virtual space created through this doubling and its architectonic characteristics. The design ambition of this thesis is to construct an architectural fiction that engages with this doubling. If architecture has the capacity to embody the ambitions and anxieties of society, the work produced attempts to invoke, through choreographed doublings manifested by the movement of figure and light through constructions in time, that human condition of desire that is concerned with finding/defining itself in the unknown, not to provide an answer for what the unknown is, but to engage with its enigmatic nature. By engaging in the protean dynamics of doubling and desire, this thesis attempts to poeticize the interval between the body and its built environment.