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This paper presents a novel approach to passivity enforcement of multiport frequency-dependent rational models. The strategy employs graph theory, and convex optimization to extend the positive-definite matrix completion problem to passive system completions. The main distinguishing feature of the proposed technique relative to those in the literature is that passivity compensations are only applied to those parameters contributing to passivity violations, in a completion manner. Numerical experiments using actual transformer data reveals that the proposed method results in models with increased accuracy.

When it comes to estimating dynamics (modes) of interconnected power systems through transient (ringdown) data, one can expect more realistic estimates for the compound system if multiple signals distributed over different locations can be used simultaneously during modal estimation. In this context, the contributions of this paper are twofold. First, it is proposed a multi-signal ringdown time-domain Vector Fitting method (‘multi-signal RTD-VF’) which is capable of modeling multiple ringdown signals simultaneously. This method appropriately generalizes the ‘single-signal RTD-VF’ method, which processes ringdown signals independently, i.e., one at a time. Second, it is proposed an instrumental variable (IV) extension of multi-signal RTD-VF. By guaranteeing local optimums after algorithm convergence, such a ‘multi-signal IV-RTD-VF’ formulation may be used for further refining estimates provided by regular multi-signal RTD-VF. Two numerical examples are used to validate the proposed methods. The first example considers actual ringdown data sets extracted from North American Eastern Interconnection (NAEI) and Brazilian Interconnected Power (BIP) systems, whereas the second one considers mode identification of a synthetic signal corrupted by colored noise.

This paper addresses the problem of obtaining optimal black-box models of n-terminal power transformers for transient studies. We use actual transformer data to demonstrate that a transformer model endowed with both passivity and symmetry can be optimally achieved only if the corresponding constraints are formulated and simultaneously included in the parameter estimation problem as opposed to enforcing them independently as two distinct post-processing model adjustments.

Wireless Power Transfer (WPT) is a technique employed to transmit energy from a source to one or multiple loads, usually located over short distances by near-field inductive coupling. An inductive link is connected to each load through a matching capacitive network for tuning the system and optimizing the whole power transfer process. However, when the frequency of the source is constant and the capacitive compensation network is fixed, the optimized operating points can only be guaranteed for fixed loads and for fixed positions between the transmitting and receiving coils. When the coils are nonstationary or the load is variable, the tuning of the inductive link must be dynamically adjusted to maintain a constant output power. This work presents the development of an optimized WPT control method for the application of an endoscopy procedure, guaranteeing sufficient energy to power a camera and a communication system allocated inside a small capsule of $\approx 26.1\times 9$ mm. A 3D receiver coil is located inside the capsule moving freely inside a transmitter coil with 400 mm diameter through translations and revolutions. The proposed system compensates disturbances such as the mechanical misalignments between the transmitter and three receiver coils in quadrature and load variation, by the dynamic adjustment of both the frequency and the matching compensation network. Preliminary results have shown reasonable power levels on the load even with low magnetic coupling coefficients ( $k\ \approx\ 0.002$ ), featured by the experimental endoscopic arrangement, providing energy to power the electronics to collect and transmit data to an external receiver.

Nos últimos anos, o cálculo de ordem fracionária ganhou muita atenção, especialmente no campo da teoria de sistemas dinâmicos e do projeto de sistemas de controle devido a possibilidades mais precisas de modelagem e aprimoramento de controle. Os controladores de ordem fracionária permitem expandir as possíveis ações de controle, para que o projetista tenha uma gama mais ampla de possibilidades de escolha para seu projeto. No entanto, os graus de liberdade são acompanhados com uma complexidade na síntese. Dentro dessa perspectiva, esse trabalho propõe duas comparações: (i) para uma planta de ordem inteira, compara-se um controlador PID de ordem inteira com um controlador CRONE de primeira geração; (ii) para um modelo baseado em uma planta real, compara-se um controlador PID de ordem inteira com um controlador PID de ordem fracionária. Na primeira análise comparativa, o controlador CRONE apresentou uma maior robustez, porém apresentou um esforço de controle maior do que o controlador PID. Já na segunda análise comparativa, os controladores FOPID e PID apresentaram desempenho semelhante, porém o controlador FOPID apresentou um esforço de controle levemente inferior.

Este artigo apresenta uma nova metodologia de ponderação para algoritmos de identificação de sistemas baseados em subespaços com especial interesse em modelar sistemas ressonantes a partir de dados de resposta em frequência. Nos métodos de identificação baseados em subespaços, a projeção ortogonal das amostras de resposta em frequência revela informações intrínsecas sobre o sistema, que são posteriormente usadas para calcular os parâmetros do modelo. A novidade apresentada nesse trabalho é a adição de uma matriz de ponderação aos dados, com o objetivo de obter uma aproximação precisa entre a resposta em frequência do modelo e os dados de medição. Dois estudos de caso, baseado em dados de medição, são apresentados para mostrar as propriedades da metodologia descrita. No primeiro, o algoritmo é usado para estimar um modelo matemático para uma estrutura de viga flexível, que apresenta alta característica de ressonância. Os resultados são comparados com um modelo estimado usando a função do Matlab, o N4SID. O algoritmo proposto mostrou um desempenho superior com base no índice RMSE. No segundo estudo de caso, o método é utilizado para estimar um modelo baseado em dados de admitância de um transformador de potencial que opera na Usina Hidrelétrica de Jirau, no Brasil.

Este trabalho apresenta uma análise de resposta dinâmica de microrredes trifásicas operando ilhadas através de um controle apropriado, cuja função é controlar a tensão e a frequência no caso de ilhamento a fim de manter o perfil dessas duas grandezas em patamares operativos semelhantes de quando conectadas. Para isso, uma proposta de controle decentralizado é apresentada. Nesta microrrede, os inversores fazem a conexão de sistemas de geração e armazenamento de energia. O controle droop empregado na operação de geradores síncronos é utilizado para que estes inversores conectados à microrrede compartilhem a potência demandada pelas instalação ou pelas cargas operando de duas formas: na primeira o inversor mede a potência ativa e reativa do sistema e com esses sinais gera a frequência, bem como a referência de tensão, e na segunda a tensão e a frequência são medidas e assim, determinando o quanto de potência ativa e reativa é injetada por este inversor. Para avaliar a resposta dos inversores operando ilhados, uma curva de carga é considerada. Resultados mostram a operação dos dois inversores em paralelo compartilhando potências ativas e reativa de acordo a variação de carga, garantindo eficiência ao controle utilizado.

This paper proposes an optimal and unifying instrumental variable (IV)-based Vector Fitting (VF) method for frequency-domain (FD) identification of models formed by rational basis function (RBF) expansions. The proposed method is denoted by IV-FD-VF and can be similarly applied for estimating models formed by both continuous- and discrete-time RBF sets. The key advantage of IV-FD-VF lies in the fact that, differently from standard FD-VF approaches, it guarantees an optimal solution after convergence. This important optimality property is proved to be independent of the nature of the noise that corrupts the data (for instance, if it is white or coloured). Two case studies are used to validate the proposed IV-FD-VF method. One of these case studies considers actual frequency-domain data sets extracted from two different single-phase power transformers.

In this paper, a methodology using Differential Evolution algorithm is proposed for the definition of the discourse universe of the fuzzy PID controller structure variables, for the load/frequency control in power systems, with this eliminating the need of the expert in systems speed control of hydraulic turbines. This controller uses the fuzzy system to tune the PID controller in real time. A mathematical model, based upon constructive information and where nonlinearities are taken into account, of a hydroelectric plant belonging to the Brazilian Interconnected System is presented as a test benchmark to analysis the proposed controller performance under various loading conditions. Simulation results are introduced to show the performance of the developed controller in comparison with a PID controller tuned by a Differential Evolution Algorithm and with a PID algorithm similar to the one actually used in the field. These results denote that the proposed controller offer better performance over others in terms of settling times and oscillation of the frequency.

This paper introduces the setup of a Distributed Generation Microgrid (DGM) focused onto research on monitoring (electrical and environmental), controling and operating microgrids. This network is located at the Polytech Center Campus of the Federal University of Parana (UFPR), in Curitiba, Brazil. It consists of nine 13.8 kV feeders, three Distributed Generation (DG) sources and is connected to the main utility through a 69 kV distribution substation of the state electricity company named as Copel DIS. In addition to the electrical configuration, all components of the microgrid are presented, such as power generation, electrical and environmental meters, $\mu \mathrm{PMU}$ and the system monitoring computing environment.

The customary approach to modelling voltage transformers (VT) at gas insulate substations (GIS) subject to Very Fast Transients Overvoltages (VFTOs) consists of using a capacitive element. However, this simplistic approach is unable to reproduce characteristic resonances and predict how transients propagate to the VT’s secondary winding. Multiport black-box passive rational models built upon frequency-domain measurements are instrumental to overcome these challenges. The adoption of S-Parameters is an alternative to black-box approaches based on Y-Parameters so as to consistently expand the measurement frequency range which is required to accurately capture the dynamics involved in VFTO phenomena up to 50 MHz at GIS facilities. We validate our results using an strategy that entails measuring, modeling and simulation upon a multiport extra-high voltage (EHV) VT. The proposed approach using S-Parameters leads to increased accuracy compared with the customary approaches using Y-Parameters or capacitive elements. We build two models estimated upon distinct frequency ranges to highlight the importance of extending the measurements into the MHz range so that time-domain simulations better reproduce VFTO characteristics. The black-box VT model thus obtained is incorporated into an actual 500 kV GIS model for transient simulations enabling the analysis of transient propagation between primary and secondary windings.

Estimating electromechanical oscillations of power systems plays a crucial role to infer about their stability. In this paper it is proposed a new method in which dominant dynamics of power systems are inferred by analyzing pole estimates produced by several regularized robust recursive least squares (R3LS) implementations operating individually — each one with a different set of autoregressive moving average exogenous (ARMAX) model orders. The proposed method is named “Clustered R3LS”, since it properly adapts the so-called K-means clustering algorithm to automatically model regions of pole agglomeration produced by these individual R3LS implementations. An additional strategy for ignoring (discarding) poles identified as spurious is also part of the proposed Clustered R3LS algorithm. From a practical point of view, Clustered R3LS is here shown to more effectively track dominant dynamics of power systems when compared to Classical R3LS, which is based on analyzing only pole estimates produced by a single R3LS implementation operating with a fixed set of ARMAX model orders that is usually hard to tune. Three case studies are here used to validate the proposed Clustered R3LS method for both ambient and ringdown data. The first and second case studies are based on two well known benchmark models proposed for the analysis of oscillatory dynamics in power systems, whereas the third case study considers actual power system data extracted during a large-scale event from the Brazilian Interconnected Power (BIP) system.

This paper proposes a novel method, called Ringdown Time-Domain Vector Fitting (RTD-VF), for estimating electromechanical modes in interconnected power systems. Such a method authentically extends, to the context of ringdown analysis, the well known Time-Domain Vector Fitting (TD-VF) method, which has already been successfully applied within other power systems areas. The proposed method is based on a state-space discretization framework which enables ringdown events to be effectively estimated when described as artificial unit impulse responses. Moreover, RTD-VF completely avoids the necessity to perform discrete Fourier transforms (DFTs) of ringdown data sequences. Three case studies are used to validate the proposed method. One of the examples considers a synthetic test signal, whereas the other two case studies consider actual ringdown data sets extracted from the North American Eastern Interconnection (NAEI) system and from the Brazilian Interconnected Power (BIP) system.

The separation of low-frequency electromechanical modes from spurious modes is a challenging problem in ambient data analysis performed by identification and signal processing techniques. In order to accurately extract all relevant dynamic properties from power system ambient measurement data, a filtering pre-processing step and high-order models are usually adopted during the identification process for an effective handling of noise, which may lead to the appearance of the so-called spurious modes into the identified system model. These spurious modes have no physical relevance, but they may present similar characteristics to the low-frequency electromechanical modes, affecting the accurate evaluation of the latter ones. Hence, this paper proposes a post-processing technique based on modal stabilization diagrams to distinguish low-frequency electromechanical modes and spurious modes from ambient data analysis. This proposed post-processing technique is applied in simulated and real measured ambient data with the purpose of estimating the dynamic properties of inter-area modes in large interconnected generation and transmission power systems (IPSs), as well as, global and local modes in active distribution networks (ADNs) with distributed generation (DG) based on synchronous generators.

This paper presents a subspace-based frequency identification method with passivity enforcement via Linear-Matrix Inequalities to achieve a globally optimal and passive solution. These inequalities are formulated and solved as a convex optimization problem using a state-of-the-art numerical solver. We also apply a weighting methodology to the LMI formulation to enhance the approximation between the model and the measured frequency-domain data. A case study including a coaxial cable experimental setup is presented to showcase and validate the proposed approach. Our results reveal that the proposed algorithm solves passivity enforcement problems in a single step without interactions and is able to successfully estimate passive models within the subspace framework.

This paper proposes a general and unifying method for constructing rational basis functions (RBFs). The proposal is specifically designed for finding characterizing matrices of linear RBF state equations. Analytic expressions for the realization of such state-space matrices are also developed. As will be shown, the construction method here proposed can be easily applied (but is not limited) to several RBF sets which have been successfully adopted in both linear and nonlinear system identification areas.

Vector fitting (VF) algorithms have become popular and powerful tools for estimating models formed by rational basis function (RBF) expansions. In this paper, we first translate the well-known continuous time-domain VF method (cTD-VF) to a discrete time-domain framework. We denote this new domain VF method by dTD-VF. Differently from the cTD-VF, the dTD-VF formulation does not rely on a numerical approximation of convolution integrals and, as a result, it can be easily implemented with a variety of RBF sets. The second part of this paper shows that the proposed dTD-VF can also be transformed into a novel instrumental variable (IV)-dTD-VF technique, which is shown to have a guaranteed optimal solution at convergence. Moreover, this important optimality property does not depend on the nature of the noise that corrupts the data (for instance, if it is white or colored). Two case studies highlight the advantages of using the proposed methods. One of these examples consists of modeling the admittance characteristics of a power system implemented as a frequency-dependent network equivalent (FDNE).

This paper presents the results of an ongoing project involving the monitoring of micro phasor measurement units (micro-PMU) at Universidade Federal do Parana (UFPR). The infrastructure for receiving, processing and storing data from micro-PMUs, energy meters and power quality analysers was deployed. Free software were tested for data storage and visualization. We modified an existing open-source Energy Monitoring System (EMS) for proper use with PMUs. The software native communication protocol was adapted from hypertext protocol (HTTP) to direct reception of user datagram protocol (UDP) in IEEE Std. C37.118.2-2011 format to receive micro-PMUs transmission at 120 phasor per second. We modified data storage format to efficiently deal with time information allowing reduction of 60% of storage space in comparison to commonly used phasor data concentrator (openPDC).

Procedures for modeling potential/ power transformers and reactors in order to support electromagnetic transients analysis have been addressed by many recent researchers. Among all methods for obtaining black-box models, the Frequency Domain Vector Fitting (FD-VF) or, simply VF, has several successful application reports in electrical power systems. This article is two purpose: first to describe a procedure that combines instrumental variables to the VF method, called the Instrumental Variable VF (IV-VF). The main advantage of the IV-VF is the fact that if the iterative procedure converged, an optimal local solution is obtained. Also to present a case study based on actual single-phase transformer measurements (Benchmark JWG A2/C4.52 for Power Transformers) and a GIS installation, where IV-VF is used for evaluating internal resonances in power transformers due to high frequency transients. The knowledge of external and internal winding response to these transients contribute to insulation design and reliability in field operation.

This paper presents a nonlinear model for a hydraulic amplifier, a component of the governor in the speed control loop of hydroelectric power plants. The amplifier transforms the electrical signal of the controller into mechanical movement of the turbine components, including the switching characteristics. This model is used to propose, on the one hand, a Piecewise Affine (PWA) representation for the hydraulic amplifier, and, on the other hand, a methodology for estimating the model parameters using field measurements, which facilitates its practical implementation. This representation is referred to as semi-physical because the model parameters are closely related with the physical construction of the hydraulic amplifier components. Among the advantages of this PWA representation are the appropriate structuring for use in system identification methods, for estimating its parameters, and the existence of advanced control techniques that use this structure in controller design, thereby improving the load-frequency control performance. The paper concludes with a description of the results, including the parameters that were estimated by using the hydraulic amplifier model with PWA structure.

The passivity enforcement problem is always a key issue when estimating passive black-box models. Passivity has been broadly scrutinized in the literature and is known to yield stable time-domain simulation; thus, passivity is a much-desired model property which cannot remain an unmodeled effect. Many successful algorithms exist that accomplish the enforcement task by model parameter perturbation, with the caveat that the passivity violations to be suppressed are not large. This paper proposes a novel approach to address this issue. We propose four different mathematical formulations to preprocess the measurement of admittance parameters. Preprocessed data serve as inputs to parameter estimation algorithms to obtain passive models for power system applications. This approach is particularly adequate to use with model parameter perturbation methods. Three examples containing data of actual measurements illustrate the advantage of using the proposed methodology.

Power system transient analysis relies on the deployment of models with ever-increasing accuracy that represents the system's frequency-dependent components with respect to the phenomena being scrutinized. A key issue in this context is the construction of a model yielding passive properties for each system component. Such properties are commonly compromised by corrupting noise in measurements that serve as input to the computation of blackbox models. In addition, model passivity could be further compromised during parameter estimation. This paper undertakes the task of developing a proposal to build an optimization methodology based on linear matrix inequalities (LMI-based) which is intended for obtaining an optimal, passive, blackbox model for a $n$ -terminal power transformer. The resulting model can be further realized as a circuit representation in order to simulate the transformer dynamics in an Eletromagnetic Transient program (EMT type). The results derived from two operating power transformers are later provided to confirm how effective the procedure herein proposed is.

In this paper, a Takenaka---Malmquist---Volterra (TMV) model structure is employed to improve the approximations in the low-pass equivalent behavioral modeling of radio frequency (RF) power amplifiers (PAs). The Takenaka---Malmquist basis generalizes the orthonormal basis functions previously used in this context. In addition, it allows each nonlinearity order in the expanded Volterra model to be parameterized by multiple complex poles (dynamics). The state-space realizations for the TMV models are introduced. The pole sets for the TMV model and also for the previous Laguerre---Volterra (LV) and Kautz---Volterra (KV) models are obtained using a constrained nonlinear optimization approach. Based on experimental data measured on a GaN HEMT class AB RF PA excited by a WCDMA signal, it is observed that the TMV model reduces the normalized mean-square error and the adjacent channel error power ratio for the upper adjacent channel (upper ACEPR) by 1.6 dB when it is compared to the previous LV and KV models under the same computational complexity.

This article presents a novel approach to analysing a power transformer’s internal response to system transients. In this approach a hybrid modelling methodology is adopted which leverages the distinct advantages offered by both Black and Grey Box modelling techniques. The Black Box model of the transformer is used within the EMTP system study environment in order to take advantage of its mathematical flexibility and modelling accuracy. Transients derived from network switching operations within the study can then be used for injection tests within the Grey Box modelling environment. The Grey Box model, which is based upon the physical structure of the transformer, will facilitate analysis of the transformer’s internal voltage response to the external stimulus. A fundamental difference between the approach described in this paper and more traditional approaches is that it does not require prior knowledge of the internal geometry of the transformer. All of the modelling parameters are derived from external tests, nameplate details and an intrinsic understanding of common transformer design principles. This can be a distinct advantage since in most cases a transformer’s design specifications are not readily available outside of the laboratory due to the manufacturer’s intellectual property restrictions. A study of a gas insulated substation within a hydroelectric power plant in Brazil is used to demonstrate the proposed technique.

Modeling dynamic systems from frequency-domain data is a relevant issue for system analysis in many fields of engineering. Many techniques to approach such problems are based on models built by using a series of rational functions with fixed structure. As far as rational basis function models are concerned, the problem of selecting the function’s poles or dynamics is an issue and the Sanathanan–Koerner (SK) iterations represent a successful approach that can be applied to solve it. Frequency localizing basis function (FLBF) models are a rational function which can be used in such a context. However FLBF, as originally proposed, are not suitable for application with SK-iterations because they have not been designed to incorporate complex poles. To overcome this issue and to make the application of SK-iterations possible, this paper proposes a new set of basis functions that are based on the FLBF. In this paper, they will be referred to as complex frequency localizing basis functions. This new set of basis functions preserves the FLBF behavior but real and complex poles can be incorporated in the model and so it allows the use of SK-iterations. The proposed model is validated through simulation.

Nesse artigo, esta sendo aplicado um controlador proporcional, integral e derivativo (PID) de ordem fracionaria para o problema de carga e frequencia de um sistema eletrico de potencia. Este controlador possui cinco parâmetros que devem ser sintonizados; assim, ele proporciona mais dois graus de liberdade em relacao ao controlador PID convencional. Para a sintonia deste controlador, o algoritmo de evolucao diferencial e utilizado. Este algoritmo e uma metaheuristica baseada na teoria da evolucao natural, sendo uma tecnica de busca, relativamente simples tendo como operacoes basicas: a selecao, a reproducao, o cruzamento e a mutacao. Para analise do controlador proposto, o modelo matematico de uma usina hidreletrica baseado em informacoes construtivas de uma planta pertencente ao Sistema Interligado Nacional sera utilizado neste trabalho. Compara-se este controlador PID fracionario com o controlador PID convencional que esta em uso na usina hidreletrica sob estudo e tambem faz-se a comparacao do controlador PID fracionario com um controlador PID convencional sintonizado pelo algoritmo de evolucao diferencial. Resultados de simulacao validam a estrategia apresentada frente aos controladores PID convencionais.

This paper presents practical results from the application of a new synthesis methodology based on model predictive control (MPC) applied to a three-wheeled omnidirectional mobile robot seeking to follow pre-established trajectories. The approach is based on the definition of an objective function with a finite future time horizon. The resulting optimization problem is declared in the form of linear matrix inequalities (LMIs). The closed loop stability of the system is guaranteed through constraints related to the non-increasing monotonicity of the objective function. Constraints are also implemented in the manipulated variables with the objective of adapting the control system to the physical limitations of the robot. Additionally, methods are described to handle the computational delay in the robot model and to adjust the control law to improve the global performance of the system.

The wireless power transfer can be useful in applications where the use of cables for powering remote devices are not suitable or even feasible. However, the transferred power is highly dependent on the relative position between the transmitter and receiver coils as well as other circuit parameters. The use of adaptive matching network in wireless power transfer system allows to track the maximum power transferred to the load while changing the magnetic coupling between the coils. By controlling both, the excitation frequency and matching capacitor at the primary side of the inductive link, the output power can be hold approximately constant over a large range of magnetic coupling coefficient. This paper presents an automatic compensation method for an inductive power transfer system. The proposed method monitors the magnitude and phase of the input current of the inductive link. Thus, the data is processed by a fuzzy logic controller actuating on the working frequency and on the matching capacitor in order to maximize the power transferred to the load. The proposed method can tolerate the movement of remote devices while charging processes.

This paper presents a new method using Fuzzy Logic for generating lightning warnings. This methodology uses information about the atmospheric electric field, the lightning dynamics around the interest site, and lightning distance information. This information was combined by Fuzzy Logic which had its membership functions optimized by Genetic Algorithms. In addition, the objective function was composed by multiple performance indicators proposed in this work. The proposed methodology was tested in a case study, generating lightning warning for an oil refinery. The results were compared with other methods and it was concluded that this new method shows up to be the most effective in generating lightning warnings.

The paper presents a methodology for state feedback MPC synthesis applied to the trajectory tracking control problem of a three wheeled omnidirectional mobile robot. The MPC design used here is based on a cost function developed over finite horizon and LMI framework. It is shown that MPC concepts well established for robot applications, for instance, the use of open loop predictions, receding horizon control, constraints manipulation, are preserved in this new formulation. The stability of the closed loop system is guaranteed by LMI conditions related with the cost function monotonicity. Simulation results of navigation are provided to demonstrate the performance of the proposed control strategy.

Este artigo trata o problema de controle de conforto térmico para ocupantes de edificações. Conforto térmico e um conceito de difícil definição e neste trabalho, utilisa-ze índice PMV (Predicted Mean Vote) para sua avaliação. Através deste índice, duas estratégias de controle preditivo caracterizadas por ter restrições terminais, denominadas aqui de PMV-MBPC (PMV Model Based Predictive Controller), são apresentadas. Na primeira estratégia, a gestão do conforto termico é realizada através da geração de sinais de referêsencia para o controlador, que otimiza o valor de PMV dentro de uma zona térmica da edificação. Na segunda, o modelo de PMV está incluso nos cálculos de previsão do controlador, gerando um modelo não-linear com estrutura Wiener. Resultados relacionados com a garantia de estabilidade do sistema em malha fechada são propostos. Neste contexto, um ambiente para testes (célula-teste) de sistemas de controle é descrita e a primeira abordagem é então implementada em tempo real neste ambiente usando um aquecedor a óleo. Resultados experimentais ilustram o desempenho do sistema controle para conforto térmico. Adicionalmente, resultados de simulação, conduzidos com dados climáticos horários, ilustram também o desempenho dos algoritmos de controle. The present paper is focused on thermal comfort control problem for building occupants. Thermal comfort is a concept difficult to define and, here, the PMV (Predicted Mean Vote) index is used by means of two predictive control strategies, characterized by having terminal constraints, called here PMV-MBPC (PMV Model Based Predictive Controller). The first thermal comfort control strategy is based on generating a temperature set-point signal that optimizes the building (single zone) internal PMV value. The second one includes the PMV model in the controller prediction computations, generating a non-linear PMV model having Wiener structure. Results related to closed loop system stability are proposed. In this context, an environment for control systems tests is described and the first approach is then implemented in real time using an oil-heater and this environment. Experimental results illustrate the thermal comfort control system performance. Additionally, simulation results, conducted with hourly weather data, also illustrate the control algorithms performance.

Automated test systems (ATS) are application-specific systems that help in enhancing reliability and productivity of testing activities in different stages of product development cycle and manufacturing. Different tests are necessary to anticipate and validate a product and its sub-systems performances along the development cycle. Moreover, in the production phase, quality control systems that perform up to 100% inspection may be present and the integration of test results with company's management systems may be required. In this paper, the design of ATS is analyzed under the light of design methodologies, which can basically be understood as procedures stating stages and intermediary results to discipline the development process of technical systems. Usually, design methodologies are presented as a set of prescribed actions and recommendations, kept with some degree of generality to allow their compatibility with a wide range of industrial products, based on different technology branches. In this paper, an adaptation of Pahl and Beitz's approach allowed the derivation of a reference model to ATS development. Proposed stages are explained considering ATS typical division, i.e.: physical system, hardware and software. The adapted methodology, in which prescriptions are made with an intermediary degree and directed to one particular kind of system, can make easier the design of a highly context-dependant kind of system, helping the design team to consider important aspects in decisions taken along the development process and keeping these decisions linked to the requirements defined in early stages. Through the use of the proposed methodology, shorter development cycles are expected, since less design loops tend occur in a higher-systematization environment.

The present work is focused on the study of indoor thermal comfort control problem in buildings equipped with HVAC (heating, ventilation and air conditioning) systems. The occupants’ thermal comfort sensation is addressed here by the well-known comfort index known as PMV (predicted mean vote) and by a comfort zone defined in a psychrometric chart. In this context, different strategies for the control algorithms are proposed by using an only-one-actuator system that can be associated to a cooling and/or heating system. The first set of strategies is related to the thermal comfort optimization and the second one includes energy consumption minimization, while maintaining the indoor thermal comfort criterion at an adequate level. The methods are based on the model predictive control scheme and simulation results are presented for two case studies. The results validate the proposed methodology in terms of both thermal comfort and energy savings.

This work is focused on developing control algorithms for hygrothermal process represented by mixed logical and dynamical models. An example is the closed newborn incubator. Such kind of system promotes a controlled micro-climate, with small heat transfer between the premature and the environment, leading to comfortable and healthful environment. This device can be represented by a piece-wise linear model having discrete values for the input signal range. A hybrid predictive control scheme is proposed for such a process. The algorithm can also be applied to different dynamic systems having the same characteristics and it is easy to be implemented in real-time environments. System identification results based on orthonormal basis functions for finding the set of linear transfer functions are discussed. Closed-loop control experiments, by using an actual laboratory pilot plant (full scale), are performed and presented to validate the proposed method.

Regression equations can be used for predicting indoor air temperature, relative humidity and energy consumption in an easier and more rapid way than building energy simulation tools. The independent variables, that is, the input data, are heating, ventilation and air conditioning (HVAC) power, outdoor temperature, relative humidity and total solar radiation. The present methodology for obtaining the regression equations is based on defining a couple of linear Multiple-Input/Single-Output (MISO) models, since two main outputs are involved, that is, indoor temperature and relative humidity. The methodology has been tested for the low- and high-thermal mass cases of the BESTest model (cases 600 and 900) and the output data is generated by using a building hygrothermal simulation tool. Validation procedures have shown very good agreement between the regression equations and the simulation tool for both winter and summer periods.

The present work is focused on the study of indoor thermal comfort control problem in buildings equipped with heating systems. The occupants' thermal comfort sensation is addressed here by a comfort index known as PMV (Predict Mean Vote). In this context, two control algorithms are proposed by using only-one-actuator system associated to the heating system. The methods are based on the model predictive control scheme and on the improvement of the PMV index together with energy minimization. Simulation results - obtained by using the weather data file for the city of Curitiba, Brazil - for two case studies: i) variations between low and moderate occupants metabolic rates and ii) variations between moderate and high occupants metabolic rates are presented to validate the proposed methodology in terms of room air temperature, relative humidity and PMV control.

This work is focused on hygrothermal control problem of closed newborn incubators. Such incubator promotes a controlled micro-climate, with small heat transfer between the premature and the environment, which promotes a healthful environment. In this context, the main hygrothermal informations are temperature, relative humidity and partial vapor pressure where the last one is closed related with the water loss by transpiration. A model predictive control scheme for this problem is presented and real time closed-loop control examples validate the proposed method in such a context. The identification procedure, a need in model based control schemes, is based on the use Laguerre functions and the experiments are performed on a laboratory pilot plant (full scale) which was built to evaluate control algorithms in incubators.

This work is focused on temperature and humidity control problem of closed newborn incubators. Such incubator promotes a controlled micro-climate, with small heat transfer between the premature and the environment, leading to a healthful environment. In this context, a laboratory pilot plant (full scale) was built to evaluate control algorithms and this plant is presented here. Furthermore, some identification results based on the use of orthonormal basis functions are discussed and a control scheme is also proposed. This control law is based on multiple local models and predictive control ideas. Closed-loop control examples validates the proposed method.

This work is focused on indoor thermal comfort control problem in buildings equipped with HVAC (Heating Ventilation and Air Conditioning) systems. The occupants thermal comfort is addressed here by a comfort zone in the psychometric chart and the PMV (Predict Mean Vote) index. In this context, three control algorithms are proposed by using only-one-actuator system associated to a heating equipment. The methods are based on the model predictive control scheme and on the improvement of indices related to occupants thermal comfort sensation. Simulation results – obtained by using the weather data file for the city of Curitiba, Brazil – are presented to validate the proposed methodology in terms of room air temperature, relative humidity and PMV control.

This paper addresses the problem of closed-loop position control and trajectory tracking of nonholonomic unicycle-type mobile robots with visual-based navigation. The robot focused here has two coaxial wheels, each one driven by an independent DC-motor. The control problem is solved by a Nonlinear Model Based Predictive Controller (NLMBPC) which contains some modifications in order to deal with the system characteristics and improve its tracking behavior. Stability properties of the closed loop system are analyzed and a comparison with a linear MBPC is presented. An actual nonholonomic robot platform, the Khepera robot, is used to validate the proposed strategy.

A new, analytical, orthonormal basis functions (OBF)-based design methodology for adaptive predictive constrained control of open-loop stable, possibly nonminimum phase, time-varying Wiener and Hammerstein systems is presented. A linear adaptive least-squares parameter estimation algorithm is applied both to a nonlinear static part and a linear dynamic, OBF-modeled factor of the Wiener/Hammerstein system. A notion of inverse systems is crucial for linear estimation of both Wiener and Hammerstein systems, with in verses of the nonlinear or linear parts respectively involved. The adaptive estimator is coupled with a simple but robust, predictive control strategy called Extended Horizon Model Algorithmic Control, with input/output constraints handled in a trivial way. Simulation examples demonstrate computational and numerical effectiveness of the new adaptive nonlinear constrained control approach.

An Orthonormal Basis Functions (OBF) approach is effectively used in adaptive parameter estimation of linear(ized) open-loop stable, possibly nonminimum phase plants with time-varying delay. In particular, discrete Laguerre models are considered in detail. A special attention is paid to the numerical conditioning issue in case of ’poor’ excitation of a plant under control, where OBF models are of particular value. Closed-loop predictive control simulations confirm the usefulness of adaptive OBF modelling, especially for systems with time-varying delays.

The present work focuses on robust predictive control (RPC) of uncertain processes and proposes a new approach based on orthonormal series function modeling. In such unstructured modeling, the output signal is described as a weighted sum of orthonormal functions that uses approximative information about the time constant of the process. Due to an efficient uncertainty representation, this kind of modeling is advantageous in the RPC context, even for constrained systems and processes with integral action. The stability of the closed-loop system is guaranteed by the setting of sufficient conditions for the selection of the controller prediction horizon. Simulation results are presented to illustrate the performance of this new RPC algorithm.