899 resultados para uncertanin nonholonomic dynamic system


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The centralized paradigm of a single controller and a single plant upon which modern control theory is built is no longer applicable to modern cyber-physical systems of interest, such as the power-grid, software defined networks or automated highways systems, as these are all large-scale and spatially distributed. Both the scale and the distributed nature of these systems has motivated the decentralization of control schemes into local sub-controllers that measure, exchange and act on locally available subsets of the globally available system information. This decentralization of control logic leads to different decision makers acting on asymmetric information sets, introduces the need for coordination between them, and perhaps not surprisingly makes the resulting optimal control problem much harder to solve. In fact, shortly after such questions were posed, it was realized that seemingly simple decentralized optimal control problems are computationally intractable to solve, with the Wistenhausen counterexample being a famous instance of this phenomenon. Spurred on by this perhaps discouraging result, a concerted 40 year effort to identify tractable classes of distributed optimal control problems culminated in the notion of quadratic invariance, which loosely states that if sub-controllers can exchange information with each other at least as quickly as the effect of their control actions propagates through the plant, then the resulting distributed optimal control problem admits a convex formulation.

The identification of quadratic invariance as an appropriate means of "convexifying" distributed optimal control problems led to a renewed enthusiasm in the controller synthesis community, resulting in a rich set of results over the past decade. The contributions of this thesis can be seen as being a part of this broader family of results, with a particular focus on closing the gap between theory and practice by relaxing or removing assumptions made in the traditional distributed optimal control framework. Our contributions are to the foundational theory of distributed optimal control, and fall under three broad categories, namely controller synthesis, architecture design and system identification.

We begin by providing two novel controller synthesis algorithms. The first is a solution to the distributed H-infinity optimal control problem subject to delay constraints, and provides the only known exact characterization of delay-constrained distributed controllers satisfying an H-infinity norm bound. The second is an explicit dynamic programming solution to a two player LQR state-feedback problem with varying delays. Accommodating varying delays represents an important first step in combining distributed optimal control theory with the area of Networked Control Systems that considers lossy channels in the feedback loop. Our next set of results are concerned with controller architecture design. When designing controllers for large-scale systems, the architectural aspects of the controller such as the placement of actuators, sensors, and the communication links between them can no longer be taken as given -- indeed the task of designing this architecture is now as important as the design of the control laws themselves. To address this task, we formulate the Regularization for Design (RFD) framework, which is a unifying computationally tractable approach, based on the model matching framework and atomic norm regularization, for the simultaneous co-design of a structured optimal controller and the architecture needed to implement it. Our final result is a contribution to distributed system identification. Traditional system identification techniques such as subspace identification are not computationally scalable, and destroy rather than leverage any a priori information about the system's interconnection structure. We argue that in the context of system identification, an essential building block of any scalable algorithm is the ability to estimate local dynamics within a large interconnected system. To that end we propose a promising heuristic for identifying the dynamics of a subsystem that is still connected to a large system. We exploit the fact that the transfer function of the local dynamics is low-order, but full-rank, while the transfer function of the global dynamics is high-order, but low-rank, to formulate this separation task as a nuclear norm minimization problem. Finally, we conclude with a brief discussion of future research directions, with a particular emphasis on how to incorporate the results of this thesis, and those of optimal control theory in general, into a broader theory of dynamics, control and optimization in layered architectures.

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This thesis is an investigation into the nature of data analysis and computer software systems which support this activity.

The first chapter develops the notion of data analysis as an experimental science which has two major components: data-gathering and theory-building. The basic role of language in determining the meaningfulness of theory is stressed, and the informativeness of a language and data base pair is studied. The static and dynamic aspects of data analysis are then considered from this conceptual vantage point. The second chapter surveys the available types of computer systems which may be useful for data analysis. Particular attention is paid to the questions raised in the first chapter about the language restrictions imposed by the computer system and its dynamic properties.

The third chapter discusses the REL data analysis system, which was designed to satisfy the needs of the data analyzer in an operational relational data system. The major limitation on the use of such systems is the amount of access to data stored on a relatively slow secondary memory. This problem of the paging of data is investigated and two classes of data structure representations are found, each of which has desirable paging characteristics for certain types of queries. One representation is used by most of the generalized data base management systems in existence today, but the other is clearly preferred in the data analysis environment, as conceptualized in Chapter I.

This data representation has strong implications for a fundamental process of data analysis -- the quantification of variables. Since quantification is one of the few means of summarizing and abstracting, data analysis systems are under strong pressure to facilitate the process. Two implementations of quantification are studied: one analagous to the form of the lower predicate calculus and another more closely attuned to the data representation. A comparison of these indicates that the use of the "label class" method results in orders of magnitude improvement over the lower predicate calculus technique.

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The optical constants of two cyanine dye films that we prepared were measured with a RAP-1-type (RAP is rotating analyzer and polarizer) spectroscopic ellipsometer. Toward making a simplified model for the wafers of a recordable compact disk (CD-R), we give their optimization designs developed with the cyanine dye films. in addition, the dynamic storage performances of two sample disks were tested by our dynamic storage testing system. Measurement results of the sample disks were obtained to test and verify our film designs. (C) 2000 Optical Society of America. OCIS codes: 160.4890, 160.4760, 210.4810.

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Biochemical energy is the fundamental element that maintains both the adequate turnover of the biomolecular structures and the functional metabolic viability of unicellular organisms. The levels of ATP, ADP and AMP reflect roughly the energetic status of the cell, and a precise ratio relating them was proposed by Atkinson as the adenylate energy charge (AEC). Under growth-phase conditions, cells maintain the AEC within narrow physiological values, despite extremely large fluctuations in the adenine nucleotides concentration. Intensive experimental studies have shown that these AEC values are preserved in a wide variety of organisms, both eukaryotes and prokaryotes. Here, to understand some of the functional elements involved in the cellular energy status, we present a computational model conformed by some key essential parts of the adenylate energy system. Specifically, we have considered (I) the main synthesis process of ATP from ADP, (II) the main catalyzed phosphotransfer reaction for interconversion of ATP, ADP and AMP, (III) the enzymatic hydrolysis of ATP yielding ADP, and (IV) the enzymatic hydrolysis of ATP providing AMP. This leads to a dynamic metabolic model (with the form of a delayed differential system) in which the enzymatic rate equations and all the physiological kinetic parameters have been explicitly considered and experimentally tested in vitro. Our central hypothesis is that cells are characterized by changing energy dynamics (homeorhesis). The results show that the AEC presents stable transitions between steady states and periodic oscillations and, in agreement with experimental data these oscillations range within the narrow AEC window. Furthermore, the model shows sustained oscillations in the Gibbs free energy and in the total nucleotide pool. The present study provides a step forward towards the understanding of the fundamental principles and quantitative laws governing the adenylate energy system, which is a fundamental element for unveiling the dynamics of cellular life.

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In this paper, a real time sliding mode control scheme for a variable speed wind turbine that incorporates a doubly feed induction generator is described. In this design, the so-called vector control theory is applied, in order to simplify the system electrical equations. The proposed control scheme involves a low computational cost and therefore can be implemented in real-time applications using a low cost Digital Signal Processor (DSP). The stability analysis of the proposed sliding mode controller under disturbances and parameter uncertainties is provided using the Lyapunov stability theory. A new experimental platform has been designed and constructed in order to analyze the real-time performance of the proposed controller in a real system. Finally, the experimental validation carried out in the experimental platform shows; on the one hand that the proposed controller provides high-performance dynamic characteristics, and on the other hand that this scheme is robust with respect to the uncertainties that usually appear in the real systems.

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In this study we employed a dynamic recurrent neural network (DRNN) in a novel fashion to reveal characteristics of control modules underlying the generation of muscle activations when drawing figures with the outstretched arm. We asked healthy human subjects to perform four different figure-eight movements in each of two workspaces (frontal plane and sagittal plane). We then trained a DRNN to predict the movement of the wrist from information in the EMG signals from seven different muscles. We trained different instances of the same network on a single movement direction, on all four movement directions in a single movement plane, or on all eight possible movement patterns and looked at the ability of the DRNN to generalize and predict movements for trials that were not included in the training set. Within a single movement plane, a DRNN trained on one movement direction was not able to predict movements of the hand for trials in the other three directions, but a DRNN trained simultaneously on all four movement directions could generalize across movement directions within the same plane. Similarly, the DRNN was able to reproduce the kinematics of the hand for both movement planes, but only if it was trained on examples performed in each one. As we will discuss, these results indicate that there are important dynamical constraints on the mapping of EMG to hand movement that depend on both the time sequence of the movement and on the anatomical constraints of the musculoskeletal system. In a second step, we injected EMG signals constructed from different synergies derived by the PCA in order to identify the mechanical significance of each of these components. From these results, one can surmise that discrete-rhythmic movements may be constructed from three different fundamental modules, one regulating the co-activation of all muscles over the time span of the movement and two others elliciting patterns of reciprocal activation operating in orthogonal directions.

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139 p.

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O reconhecimento de padões é uma área da inteligência computacional que apoia a resolução de problemas utilizando ferramentas computacionais. Dentre esses problemas podem ser citados o reconhecimento de faces, a identificação de impressões digitais e a autenticação de assinaturas. A autenticação de assinaturas de forma automática tem sua relevância pois está ligada ao reconhecimento de indivíduos e suas credenciais em sistemas complexos e a questões financeiras. Neste trabalho é apresentado um estudo dos parâmetros do Dynamic Time Warping, um algoritmo utilizado para alinhar duas assinaturas e medir a similaridade existente entre elas. Variando-se os principais parâmetros desse algoritmo, sobre uma faixa ampla de valores, foram obtidas as médias dos resultados de erros na classificação, e assim, estas médias foram avaliadas. Com base nas primeiras avaliação, foi identificada a necessidade de se calcular um desses parâmetros de forma dinâmica, o gap cost, a fim de ajustá-lo no uso de uma aplicação prática. Uma proposta para a realização deste cálculo é apresentada e também avaliada. É também proposta e avaliada uma maneira alternativa de representação dos atributos da assinatura, de forma a considerar sua curvatura em cada ponto adquirido no processo de aquisição, utilizando os vetores normais como forma de representação. As avaliações realizadas durante as diversas etapas do estudo consideraram o Equal Error Rate (EER) como indicação de qualidade e as técnicas propostas foram comparadas com técnicas já estabelecidas, obtendo uma média percentual de EER de 3,47%.

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Our ability to skillfully manipulate an object often involves the motor system learning to compensate for the dynamics of the object. When the two arms learn to manipulate a single object they can act cooperatively, whereas when they manipulate separate objects they control each object independently. We examined how learning transfers between these two bimanual contexts by applying force fields to the arms. In a coupled context, a single dynamic is shared between the arms, and in an uncoupled context separate dynamics are experienced independently by each arm. In a composition experiment, we found that when subjects had learned uncoupled force fields they were able to transfer to a coupled field that was the sum of the two fields. However, the contribution of each arm repartitioned over time so that, when they returned to the uncoupled fields, the error initially increased but rapidly reverted to the previous level. In a decomposition experiment, after subjects learned a coupled field, their error increased when exposed to uncoupled fields that were orthogonal components of the coupled field. However, when the coupled field was reintroduced, subjects rapidly readapted. These results suggest that the representations of dynamics for uncoupled and coupled contexts are partially independent. We found additional support for this hypothesis by showing significant learning of opposing curl fields when the context, coupled versus uncoupled, was alternated with the curl field direction. These results suggest that the motor system is able to use partially separate representations for dynamics of the two arms acting on a single object and two arms acting on separate objects.

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The authors demonstrate that a widely proposed method of robot dynamic control can be inherently unstable, due to an algebraic feedback loop condition causing an ill-posed feedback system. By focussing on the concept of ill-posedness a necessary and sufficient condition is derived for instability in robot manipulator systems which incorporate online acceleration cross-coupling control. Also demonstrated is a quasilinear multivariable control framework useful for assessing the robustness of this type of control when the instability condition is not obeyed.

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DNA methylation directed by 24-nucleotide small RNAs involves the small RNA-binding protein ARGONAUTE4 (AGO4), and it was previously shown that AGO4 localizes to nucleolus-adjacent Cajal bodies, sites of snRNP complex maturation. Here we demonstrate that AGO4 also localizes to a second class of nuclear bodies, called AB-bodies, which are found immediately adjacent to condensed 45S ribosomal DNA (rDNA) sequences. AB-bodies also contain other proteins involved in RNA-directed DNA methylation including NRPD1b (a subunit of the RNA Polymerase IV complex, RNA PolIV), NRPD2 (a second subunit of this complex), and the DNA methyltransferase DRM2. These two classes of AGO4 bodies are structurally independent--disruption of one class does not affect the other--suggesting a dynamic regulation of AGO4 within two distinct nuclear compartments in Arabidopsis. Abolishing Cajal body formation in a coilin mutant reduced overall AGO4 protein levels, and coilin dicer-like3 double mutants showed a small decrease in DNA methylation beyond that seen in dicer-like3 single mutants, suggesting that Cajal bodies are required for a fully functioning DNA methylation system in Arabidopsis.

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End-to-end real-time experimental demonstrations are reported, for the first time, of aggregated 11.25Gb/s over 26.4km standard SMF, optical orthogonal frequency division multiple access (OOFDMA) PONs with adaptive dynamic bandwidth allocation (DBA). The demonstrated intensity-modulation and direct-detection (IMDD) OOFDMA PON system consists of two optical network units (ONUs), each of which employs a DFB-based directly modulated laser (DML) or a VCSEL-based DML for modulating upstream signals. Extensive experimental explorations of dynamic OOFDMA PON system properties are undertaken utilizing identified optimum DML operating conditions. It is shown that, for simultaneously achieving acceptable BERs for all upstream signals, the OOFDMA PON system has a >3dB dynamic ONU launch power variation range, and the BER performance of the system is insusceptible to any upstream symbol offsets slightly smaller than the adopted cyclic prefix. In addition, experimental results also indicate that, in addition to maximizing the aggregated system transmission capacity, adaptive DBA can also effectively reduce imperfections in transmission channel properties without affecting signal bit rates offered to individual ONUs.

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The Brushless Doubly-Fed Induction Generator (BDFIG) shows commercial promise as replacement for doublyfed slip-ring generators for wind power applications by offering reduced capital and operational costs due to its brushless operation. In order to facilitate its commercial deployment, the capabilities of the BDFIG system to comply with grid code requirements have to be assessed. This paper, for the first time, studies the performance of the BDFIG under grid fault ride-through and presents the dynamic behaviour of the machine during three-phase symmetrical voltage dips. Both full and partial voltage dips are studied using a vector model. Simulation and experimental results are provided for a 180 frame BDFIG.