Uma técnica de linearização por realimentação para compensação de agarramento em válvulas de controle pneumáticas

In control loops valve stiction is a very common problem. Generally, it is one of main causes of poor performance of industrial systems. Its most commonly observed effect is oscillation in the process variables. To circumvent the undesirable effects, friction compensators have been proposed in order to reduce the variability in the output. This work analyzes the friction compensation in pneumatic control valves by using feedback linearization technique. The valve model includes both dead zone and jump. Simulations show that the use of this more complete model results in controllers with superior performance. The method is also compared through simulations with the method known as Constant Reinforcement (CR), widely used in this problem.

Generic wind estimation and compensation based on residual generators and higher-order sliding mode schemes

This master thesis proposes a solution to the approach problem in case of unknown severe microburst wind shear for a fixed-wing aircraft, accounting for both longitudinal and lateral dynamics. The adaptive controller design for wind rejection is also addressed, exploiting the wind estimation provided by suitable estimators. It is able to successfully complete the final approach phase even in presence of wind shear, and at the same time aerodynamic envelope protection is retained. The adaptive controller for wind compensation has been designed by a backstepping approach and feedback linearization for time-varying systems. The wind shear components have been estimated by higher-order sliding mode schemes. At the end of this work the results are provided, an autonomous final approach in presence of microburst is discussed, performances are analyzed, and estimation of the microburst characteristics from telemetry data is examined.

Incorporation of Hysteretic Effects in Model-Order Reduction Analysis of Magnetic Devices

The ability to predict the properties of magnetic materials in a device is essential to ensuring the correct operation and optimization of the design as well as the device behavior over a wide range of input frequencies. Typically, development and simulation of wide-bandwidth models requires detailed, physics-based simulations that utilize significant computational resources. Balancing the trade-offs between model computational overhead and accuracy can be cumbersome, especially when the nonlinear effects of saturation and hysteresis are included in the model. This study focuses on the development of a system for analyzing magnetic devices in cases where model accuracy and computational intensity must be carefully and easily balanced by the engineer. A method for adjusting model complexity and corresponding level of detail while incorporating the nonlinear effects of hysteresis is presented that builds upon recent work in loss analysis and magnetic equivalent circuit (MEC) modeling. The approach utilizes MEC models in conjunction with linearization and model-order reduction techniques to process magnetic devices based on geometry and core type. The validity of steady-state permeability approximations is also discussed.

Comparative performance analysis of drives for induction motors

The asynchronous polyphase induction motor has been the motor of choice in industrial settings for about the past half century because power electronics can be used to control its output behavior. Before that, the dc motor was widely used because of its easy speed and torque controllability. The two main reasons why this might be are its ruggedness and low cost. The induction motor is a rugged machine because it is brushless and has fewer internal parts that need maintenance or replacement. This makes it low cost in comparison to other motors, such as the dc motor. Because of these facts, the induction motor and drive system have been gaining market share in industry and even in alternative applications such as hybrid electric vehicles and electric vehicles. The subject of this thesis is to ascertain various control algorithms’ advantages and disadvantages and give recommendations for their use under certain conditions and in distinct applications. Four drives will be compared as fairly as possible by comparing their parameter sensitivities, dynamic responses, and steady-state errors. Different switching techniques are used to show that the motor drive is separate from the switching scheme; changing the switching scheme produces entirely different responses for each motor drive.

DISPATCHING AND RELOCATION OF EMERGENCY VEHICLES ON FREEWAYS: THEORIES AND APPLICATIONS

Resource allocation decisions are made to serve the current emergency without knowing which future emergency will be occurring. Different ordered combinations of emergencies result in different performance outcomes. Even though future decisions can be anticipated with scenarios, previous models follow an assumption that events over a time interval are independent. This dissertation follows an assumption that events are interdependent, because speed reduction and rubbernecking due to an initial incident provoke secondary incidents. The misconception that secondary incidents are not common has resulted in overlooking a look-ahead concept. This dissertation is a pioneer in relaxing the structural assumptions of independency during the assignment of emergency vehicles. When an emergency is detected and a request arrives, an appropriate emergency vehicle is immediately dispatched. We provide tools for quantifying impacts based on fundamentals of incident occurrences through identification, prediction, and interpretation of secondary incidents. A proposed online dispatching model minimizes the cost of moving the next emergency unit, while making the response as close to optimal as possible. Using the look-ahead concept, the online model flexibly re-computes the solution, basing future decisions on present requests. We introduce various online dispatching strategies with visualization of the algorithms, and provide insights on their differences in behavior and solution quality. The experimental evidence indicates that the algorithm works well in practice. After having served a designated request, the available and/or remaining vehicles are relocated to a new base for the next emergency. System costs will be excessive if delay regarding dispatching decisions is ignored when relocating response units. This dissertation presents an integrated method with a principle of beginning with a location phase to manage initial incidents and progressing through a dispatching phase to manage the stochastic occurrence of next incidents. Previous studies used the frequency of independent incidents and ignored scenarios in which two incidents occurred within proximal regions and intervals. The proposed analytical model relaxes the structural assumptions of Poisson process (independent increments) and incorporates evolution of primary and secondary incident probabilities over time. The mathematical model overcomes several limiting assumptions of the previous models, such as no waiting-time, returning rule to original depot, and fixed depot. The temporal locations flexible with look-ahead are compared with current practice that locates units in depots based on Poisson theory. A linearization of the formulation is presented and an efficient heuristic algorithm is implemented to deal with a large-scale problem in real-time.

Asymptotic stability of a coupled Advection-Diffusion-Reaction system arising in bioreactor processes.

In this work, we perform an asymptotic analysis of a coupled system of two Advection-Diffusion-Reaction equations with Danckwerts boundary conditions, which models the interaction between a microbial population (e.g., bacterias), called biomass, and a diluted organic contaminant (e.g., nitrates), called substrate, in a continuous flow bioreactor. This system exhibits, under suitable conditions, two stable equilibrium states: one steady state in which the biomass becomes extinct and no reaction is produced, called washout, and another steady state, which corresponds to the partial elimination of the substrate. We use the method of linearization to give sufficient conditions for the asymptotic stability of the two stable equilibrium configurations. Finally, we compare our asymptotic analysis with the usual asymptotic analysis associated to the continuous bioreactor when it is modeled with ordinary differential equations.

Multi-level, Multi-stage and Stochastic Optimization Models for Energy Conservation in Buildings for Federal, State and Local Agencies

Energy Conservation Measure (ECM) project selection is made difficult given real-world constraints, limited resources to implement savings retrofits, various suppliers in the market and project financing alternatives. Many of these energy efficient retrofit projects should be viewed as a series of investments with annual returns for these traditionally risk-averse agencies. Given a list of ECMs available, federal, state and local agencies must determine how to implement projects at lowest costs. The most common methods of implementation planning are suboptimal relative to cost. Federal, state and local agencies can obtain greater returns on their energy conservation investment over traditional methods, regardless of the implementing organization. This dissertation outlines several approaches to improve the traditional energy conservations models. Any public buildings in regions with similar energy conservation goals in the United States or internationally can also benefit greatly from this research. Additionally, many private owners of buildings are under mandates to conserve energy e.g., Local Law 85 of the New York City Energy Conservation Code requires any building, public or private, to meet the most current energy code for any alteration or renovation. Thus, both public and private stakeholders can benefit from this research. The research in this dissertation advances and presents models that decision-makers can use to optimize the selection of ECM projects with respect to the total cost of implementation. A practical application of a two-level mathematical program with equilibrium constraints (MPEC) improves the current best practice for agencies concerned with making the most cost-effective selection leveraging energy services companies or utilities. The two-level model maximizes savings to the agency and profit to the energy services companies (Chapter 2). An additional model presented leverages a single congressional appropriation to implement ECM projects (Chapter 3). Returns from implemented ECM projects are used to fund additional ECM projects. In these cases, fluctuations in energy costs and uncertainty in the estimated savings severely influence ECM project selection and the amount of the appropriation requested. A risk aversion method proposed imposes a minimum on the number of “of projects completed in each stage. A comparative method using Conditional Value at Risk is analyzed. Time consistency was addressed in this chapter. This work demonstrates how a risk-based, stochastic, multi-stage model with binary decision variables at each stage provides a much more accurate estimate for planning than the agency’s traditional approach and deterministic models. Finally, in Chapter 4, a rolling-horizon model allows for subadditivity and superadditivity of the energy savings to simulate interactive effects between ECM projects. The approach makes use of inequalities (McCormick, 1976) to re-express constraints that involve the product of binary variables with an exact linearization (related to the convex hull of those constraints). This model additionally shows the benefits of learning between stages while remaining consistent with the single congressional appropriations framework.

Transport in the blood of an anti-tumor water-soluble rutheniun cyclopentadienyl complex: a fluorescence study on albumin binding

Dissertação de mestrado, Qualidade em Análises, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2014

An alternative LQR-based excitation controller design for power systems to enhance small-signal stability

This paper presents a new approach to design excitation controller for power systems to enhance small-signal stability. Partial feedback linearization scheme is used to design the controller for a linearized power system model which transforms a part of this model into a new system through linear coordinate transformation. In this paper, the excitation control law as a function of state variables is determined from the dynamics of the partly transformed new system provided that the controller stabilizes the remaining dynamics of the system which are not transformed through feedback linearization. The stability of the remaining dynamics is also discussed in this paper. Since the proposed control scheme uses state variables as feedback, it is analogous to a linear quadratic regulator (LQR) based excitation controller. Therefore, the performance of the proposed scheme is evaluated on a single machine infinite bus (SMIB) system and compared to that of an LQR controller.

Robust partial feedback linearizing excitation controller design for multimachine power systems

This paper presents a new robust nonlinear excitationcontroller design for synchronous generators in multimachine powersystems to enhance the transient stability. The mismatches betweenthe original power system model and formulated mathematical modelare considered as uncertainties which are modeled through thesatisfaction of matching conditions. The exogenous noises appearingfrom measurements are incorporated with the power system modelincluding the two-axis model of synchronous generators. The partialfeedback linearization technique is used to design the controller whichtransforms the original nonlinear multimachine power system modelinto several reduced-order linear and autonomous subsystems. Thedesired control law is obtained for each subsystem and implemented ina decentralized manner provided that the dynamics of the autonomoussubsystems have no effects on the overall stability of the system. Theanalysis related to the dynamics of noisy autonomous subsystems isalso included and the proposed controller has the excellent capabilityto decouple these noises. Finally, the performance of the proposedcontrol scheme is evaluated on an IEEE 39-bus benchmark powersystem following different types of large disturbances. The performanceof the proposed controller is compared to that of a partialfeedback linearizing controller, which is designed without robustnessproperties, to verify the effectiveness of the proposed control scheme.

Transient stability enhancement of multimachine power systems using nonlinear observer-based excitation controller

This paper presents an approach to design a nonlinear observer-based excitation controller for multimachine power systems to enhance the transient stability. The controller is designed based on the partial feedback linearization of a nonlinear power system model which transforms the model into a reducedorder linear one with an autonomous dynamical part. Then a linear state feedback stabilizing controller is designed for the reduced-order linear power system model using optimal control theory which enhances the stability of the entire system. The states of the feedback stabilizing controller are obtained from the nonlinear observer and the performance of this observer-based controller is independent of the operating points of power systems. The performance of the proposed observer-based controller is compared to that of an exact feedback linearizing observer-based controller and a partial feedback linearizing controller without observer under different operating conditions.

Nonlinear DSTATCOM controller design for distribution network with distributed generation to enhance voltage stability

This paper presents a nonlinear controller design for a DSTATCOM connected to a distribution network with distributed generation (DG) to regulate the line voltage by providing reactive power compensation.The controller is designed based on the partial feedback linearization which transforms the nonlinear system into a reduced-order linear system and an autonomous system whose dynamics are known as internal dynamics of the system. This paper also investigates the stability of internal dynamics of a DSTATCOM as it is a basic requirement to design partial feedback linearizing controllers. The performance of the proposed controller is evaluated in terms reactive power compensation to enhance the voltage stability of distribution with DG.

Avaliação de métodos de estimação da variância em amostras complexas

A necessidade de conhecer uma população impulsiona um processo de recolha e análise de informação. Usualmente é muito difícil ou impossível estudar a totalidade da população, daí a importância do estudo com recurso a amostras. Conceber um estudo por amostragem é um processo complexo, desde antes da recolha dos dados até a fase de análise dos mesmos. Na maior parte dos estudos utilizam-se combinações de vários métodos probabilísticos de amostragem para seleção de uma amostra, que se pretende representativa da população, denominado delineamento de amostragem complexo. O conhecimento dos erros de amostragem é necessário à correta interpretação dos resultados de inquéritos e à avaliação dos seus planos de amostragem. Em amostras complexas, têm sido usadas aproximações ajustadas à natureza complexa do plano da amostra para a estimação da variância, sendo as mais utilizadas: o método de linearização Taylor e as técnicas de reamostragem e replicação. O principal objetivo deste trabalho é avaliar o desempenho dos estimadores usuais da variância em amostras complexas. Inspirado num conjunto de dados reais foram geradas três populações com características distintas, das quais foram sorteadas amostras com diferentes delineamentos de amostragem, na expectativa de obter alguma indicação sobre em que situações se deve optar por cada um dos estimadores da variância. Com base nos resultados obtidos, podemos concluir que o desempenho dos estimadores da variância da média amostral de Taylor, Jacknife e Bootstrap varia com o tipo de delineamento e população. De um modo geral, o estimador de Bootstrap é o menos preciso e em delineamentos estratificados os estimadores de Taylor e Jackknife fornecem os mesmos resultados; Evaluation of variance estimation methods in complex samples ABSTRACT: The need to know a population drives a process of collecting and analyzing information. Usually is to hard or even impossible to study the whole population, hence the importance of sampling. Framing a study by sampling is a complex process, from before the data collection until the data analysis. Many studies have used combinations of various probabilistic sampling methods for selecting a representative sample of the population, calling it complex sampling design. Knowledge of sampling errors is essential for correct interpretation of the survey results and evaluation of the sampling plans. In complex samples to estimate the variance has been approaches adjusted to the complex nature of the sample plane. The most common are: the linearization method of Taylor and techniques of resampling and replication. The main objective of this study is to evaluate the performance of usual estimators of the variance in complex samples. Inspired on real data we will generate three populations with distinct characteristics. From this populations will be drawn samples using different sampling designs. In the end we intend to get some lights about in which situations we should opt for each one of the variance estimators. Our results show that the performance of the variance estimators of sample mean Taylor, Jacknife and Bootstrap varies with the design and population. In general, the Bootstrap estimator is less precise and in stratified design Taylor and Jackknife estimators provide the same results.