No description available.

Delta-Sigma (ΔΣ) analog-to-digital converters (ADCs) are traditionally used in high quality audio systems, instrumentation and measurement (I&M) and biomedical devices. With the continued downscaling of CMOS technology, they are becoming popular in wideband applications such as wireless and wired communication systems,high-definition television and radar systems. There are two general realizations of a ΔΣ modulator. One is based on the discrete-time (DT) switched-capacitor (SC) circuitry and the other employs continuous-time (CT) circuitry. Compared to a CT structure, the DT ΔΣ ADC is easier to analyze and design, is more robust to process variations and jitter noise, and is more flexible in the multi-mode applications. On the other hand, the CT ΔΣ ADC does not suffer from the strict settling accuracy requirement for the loop filter and thus can achieve lower power dissipation and higher sampling frequency than its DT counterpart. In this thesis, both DT and CT ΔΣ ADCs are investigated. Several design innovations, in both system-level and circuit-level, are proposed to achieve lower power consumption and wider signal bandwidth. For DT ΔΣ ADCs, a new dynamic-biasing scheme is proposed to reduce opamp bias current and the associated signal-dependent harmonic distortion is minimized by using the low-distortion architecture. The technique was verified in a 2.5MHz BW and 13bit dynamic range DT ΔΣ ADC. In addition, a second-order noise coupling technique is presented to save two integrators for the loop filter, and to achieve low power dissipation. Also, a direct-charge-transfer (DCT) technique is suggested to reduce the speed requirements of the adder, which is also preferable in wideband low-power applications. For CT ΔΣ ADCs, a wideband low power CT 2-2 MASH has been designed. High linearity performance was achieved by using a modified low-distortion technique, and the modulator achieves higher noise-shaping ability than the single stage structure due to the inter-stage gain. Also, the quantization noise leakage due to analog circuit non-idealities can be adaptively compensated by a designed digital calibration filter. Using a 90nm process, simulation of the modulator predicts a 12bit resolution within 20MHz BW and consumes only 25mW for analog circuitry. In addition, the noise-coupling technique is investigated and proposed for the design of CT ΔΣ ADCs and it is promising to achieve low power dissipation for wideband applications. Finally, the application of noise-coupling technique is extended and introduced to high-accuracy incremental data converters. Low power dissipation can be expected. / Graduation date: 2010

Orientador: Celso Pascoli Bottura / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-08-04T18:33:31Z (GMT). No. of bitstreams: 1 DelRealTamariz_Annabell_D.pdf: 5783881 bytes, checksum: 21a1a2e27552398a982a934513988a24 (MD5) Previous issue date: 2005 / Resumo: Este estudo apresenta contribuições para modelagem computacional de dados multivariáveis no espaço de estado, tanto com sistemas lineares invariantes como com variantes no tempo. Propomos para modelagem determinística-estocástica de dados ruidosos, o Algoritmo MOESP_AOKI. Propomos, utilizando Redes Neurais Recorrentes multicamadas, algoritmos para resolver a Equação Algébrica de Riccati Discreta bem como a Inequação Algébrica de Riccati Discreta, via Desigualdades Matriciais Lineares. Propomos um esquema de controle adaptativo com Escalonamento de Ganhos, baseado em Redes Neurais, para sistemas multivariáveis discretos variantes no tempo, identificados pelo algoritmo MOESP_VAR, também proposto nesta tese. Em síntese, uma estrutura de controle inteligente para sistemas discretos multivariáveis variantes no tempo, através de uma abordagem que pode ser chamada ILPV (Intelligent Linear Parameter Varying), é proposta e implementada. Um controlador LPV Inteligente, para dados computacionalmente modelados pelo algoritmo MOESP_VAR, é concretizado, implementado e testado com bons resultados / Abstract: This study presents contributions for state space multivariable computational data modelling with discrete time invariant as well as with time varying linear systems. A proposal for Deterministic-Estocastica Modelling of noisy data, MOESP_AOKI Algorithm, is made. We present proposals forsolving the Discrete-Time Algebraic Riccati Equation as well as the associate Linear Matrix Inequalityusing a multilayer Recurrent Neural Network approaches. An Intelligent Linear Parameter Varying(ILPV) control approach for multivariable discrete Linear Time Varying (LTV) systems identified bythe MOESP_VAR algorithm, are both proposed. A gain scheduling adaptive control scheme based on neural networks is designed to tune on-line the optimal controllers. In synthesis, an Intelligent Linear Parameter Varying (ILPV) Control approach for multivariable discrete Linear Time Varying Systems (LTV), identified by the algorithm MOESP_VAR, is proposed. This way an Intelligent LPV Control for multivariable data computationally modeled via the MOESP_VAR algorithm is structured, implemented and tested with good results / Doutorado / Automação / Doutor em Engenharia Elétrica

IGBT based power converters acts as front end in the present day Adjustable Speed Drive (ASD). This offers many advantages and makes regenerative action possible. PWM rectifier operation produces electrically noisy DC bus on common mode basis. This results in higher ground current as compared to three phase diode bridge rectifier. Due to fast turn-ON and turn-OFF time of IGBT, the inverter output voltage dv/dt is high during switching transients and voltage waveform is rich in harmonics. As a result, in applications involving long cable the motor terminal voltage during the switching transient is as high as twice the applied voltage. This voltage stress reduces the life of insulation in motors. The high dv/dt output voltage applied at the motor terminal excites the parasitic capacitive coupling resulting in increased ground currents and causes Electric Discharge Machining (EDM) which reduces the life of motor bearings. The common mode voltage due to PWM rectifier and the inverter appear at the motor terminals exacerbating these problems. The common mode voltage due to PWM inverter with PWM rectifier is analyzed. An integrated approach for filter design is proposed wherein the adverse effects due to common mode voltage of both AFE converter and the inverter is addressed. The proposed topology addresses the problems of common mode voltage, common mode current and voltage doubling due to ASD. The design procedure for proposed filter topology is discussed with experimental results that validate the effectiveness of the filter. Inclusion of such higher order filter in the converter topology leads to problems such as resonance. Passive methods are investigated for damping the line resonance due to LCL filter and common mode resonance due to common mode filter. The need for active damping technique for resonance due to common mode filter is presented. State space based damping technique is proposed to effectively damp the resonance due to line filter and the common mode filter. Experimental results are presented that validate the effectiveness of active damping both on the line basis (differential mode) and line to ground basis (common mode) of the filter.

L’analyse et la synthèse des systèmes dynamiques ont connu un développement important au cours des dernières décennies comme l’atteste le nombre considérable des travaux publiés dans ce domaine, et continuent d’être un axe de recherche régulièrement exploré. Si la plupart des travaux concernent les systèmes linéaires et non linéaires temps continu, peu de résultats ont étaient établis dans le cas temps discret. Les travaux de cette thèse portent sur l’observation et la commande d’une classe de systèmes non linéaires à temps discret. Dans un premier temps, le problème de synthèse d’observateur d’état utilisant une fenêtre de mesures glissante est abordé. Des conditions de stabilité et de robustesse moins restrictives sont déduites. Deux classes de systèmes non linéaires à temps discret sont étudiées : les systèmes de type Lipschitz et les systèmes « one-sided Lipschitz ». Ensuite, une approche duale a été explorée afin de déduire une loi de commande stabilisante basée sur un observateur. Les conditions d’existence d’un observateur et d’un contrôleur stabilisant les systèmes étudiés sont formulées sous forme d’un problème d’optimisation LMI. L’efficacité et la validité des approches présentées sont montrées à travers des exemples académiques / The analysis and synthesis of dynamic systems has undergone significant development in recent decades, as illustrated by the considerable number of published works in this field, and continue to be a research theme regularly explored. While most of the existing work concerns linear and nonlinear continuous-time systems, few results have been established in the discrete-time case. This thesis deals with the observation and control of a class of nonlinear discrete-time systems. First, the problem of state observer synthesis using a sliding window of measurements is discussed. Non-restrictive stability and robustness conditions are deduced. Two classes of discrete time nonlinear systems are studied: Lipschitz systems and one-side Lipschitz systems. Then, a dual approach was explored to derive a stabilizing control law based on observer-based state feedback. The conditions for the existence of an observer and a controller stabilizing the studied classes of nonlinear systems are expressed in term of LMI. The effectiveness and validity of the proposed approaches are shown through numerical examples

Indiana University-Purdue University Indianapolis (IUPUI) / Yan, Jiaxiang. M.S.E.C.E., Purdue University, May 2013. Modeling, Monitoring and Optimization of Discrete Event Systems Using Petri Nets. Major Professor: Lingxi Li. In last decades, the research of discrete event systems (DESs) has attracts more and more attention because of the fast development of intelligent control strategies. Such control measures combine the conventional control strategies with discrete decision-making processes which simulate human decision-making processes. Due to the scale and complexity of common DESs, the dedicated models, monitoring methods and optimal control strategies for them are necessary. Among various DES models, Petri nets are famous for the advantage in dealing with asynchronous processes. They have been widely applied in intelligent transportation systems (ITS) and communication technology in recent years. With encoding of the Petri net state, we can also enable fault detection and identification capability in DESs and mitigate potential human errors. This thesis studies various problems in the context of DESs that can be modeled by Petri nets. In particular, we focus on systematic modeling, asynchronous monitoring and optimal control strategies design of Petri nets. This thesis starts by looking at the systematic modeling of ITS. A microscopic model of signalized intersection and its two-layer timed Petri net representation is proposed in this thesis, where the first layer is the representation of the intersection and the second layer is the representation of the traffic light system. Deterministic and stochastic transitions are both involved in such Petri net representation. The detailed operation process of such Petri net representation is stated. The improvement of such Petri net representation is also provided with comparison to previous models. Then we study the asynchronous monitoring of sensor networks. An event sequence reconstruction algorithm for a given sensor network based on asynchronous observations of its state changes is proposed in this thesis. We assume that the sensor network is modeled as a Petri net and the asynchronous observations are in the form of state (token) changes at different places in the Petri net. More specifically, the observed sequences of state changes are provided by local sensors and are asynchronous, i.e., they only contain partial information about the ordering of the state changes that occur. We propose an approach that is able to partition the given net into several subnets and reconstruct the event sequence for each subnet. Then we develop an algorithm that is able to reconstruct the event sequences for the entire net that are consistent with: 1) the asynchronous observations of state changes; 2) the event sequences of each subnet; and 3) the structure of the given Petri net. We discuss the algorithmic complexity. The final problem studied in this thesis is the optimal design method of Petri net controllers with fault-tolerant ability. In particular, we consider multiple faults detection and identification in Petri nets that have state machine structures (i.e., every transition in the net has only one input place and one output place). We develop the approximation algorithms to design the fault-tolerant Petri net controller which achieves the minimal number of connections with the original controller. A design example for an automated guided vehicle (AGV) system is also provided to illustrate our approaches.

Indiana University-Purdue University Indianapolis (IUPUI) / In recent decades, Petri nets (PNs) have been used to model traffic networks for different purposes, such as signal phase control, routing, and traffic flow estimation, etc. Because of the complex nature of traffic networks where both discrete and continuous dynamics come into play, the Hybrid Petri net (HPN) model becomes an important tool for the modeling and analysis of traffic networks. In Chapter 1 a brief historical summery about traffic systems control and then related work is mentioned followed by the major contributions in this research. Chapter 2 provides a theoretical background on Petri nets. In Chapter 3, we develop a HPN model for a single signalized intersection first, then we extend this model to study a simple traffic network that consists of two successive intersections. Time delays between different points of network are also considered in order to make the model suitable for analysis and simulation. In addition to HPN models, we also consider discrete Petri nets where their modeling simplicity enables the characterization of the occurrences of all events in the system. This discrete PN is particularly useful to give a higher-level representation of the traffic network and study its event occurrences and correlations. In Chapter 4, we build a discrete PN model to represent a traffic network with two successive intersections. However, we find that the model leads to unbounded places which cannot accurately reflect the dynamics of the traffic in terms of event occurrences. Hence, we introduce the Modified Binary Petri nets (MBPN) to overcome the limitation and resolve the confliction problem when we design our controllers. This MBPN model is a powerful tool and can be useful for the modeling and analysis of many other applications in traffic networks. Chapter 5 gives a summary for each chapter, provides conclusion and discusses future work for both discrete and hybrid Petri nets.