Robust model predictive controller with output feedback and target tracking

A model predictive controller (MPC) is proposed, which is robustly stable for some classes of model uncertainty and to unknown disturbances. It is considered as the case of open-loop stable systems, where only the inputs and controlled outputs are measured. It is assumed that the controller will work in a scenario where target tracking is also required. Here, it is extended to the nominal infinite horizon MPC with output feedback. The method considers an extended cost function that can be made globally convergent for any finite input horizon considered for the uncertain system. The method is based on the explicit inclusion of cost contracting constraints in the control problem. The controller considers the output feedback case through a non-minimal state-space model that is built using past output measurements and past input increments. The application of the robust output feedback MPC is illustrated through the simulation of a low-order multivariable system.

Robust model predictive control with zone control

Model predictive control (MPC) is usually implemented as a control strategy where the system outputs are controlled within specified zones, instead of fixed set points. One strategy to implement the zone control is by means of the selection of different weights for the output error in the control cost function. A disadvantage of this approach is that closed-loop stability cannot be guaranteed, as a different linear controller may be activated at each time step. A way to implement a stable zone control is by means of the use of an infinite horizon cost in which the set point is an additional variable of the control problem. In this case, the set point is restricted to remain inside the output zone and an appropriate output slack variable is included in the optimisation problem to assure the recursive feasibility of the control optimisation problem. Following this approach, a robust MPC is developed for the case of multi-model uncertainty of open-loop stable systems. The controller is devoted to maintain the outputs within their corresponding feasible zone, while reaching the desired optimal input target. Simulation of a process of the oil re. ning industry illustrates the performance of the proposed strategy.

Robust model-order reduction of complex biological processes

This paper addresses robust model-order reduction of a high dimensional nonlinear partial differential equation (PDE) model of a complex biological process. Based on a nonlinear, distributed parameter model of the same process which was validated against experimental data of an existing, pilot-scale BNR activated sludge plant, we developed a state-space model with 154 state variables in this work. A general algorithm for robustly reducing the nonlinear PDE model is presented and based on an investigation of five state-of-the-art model-order reduction techniques, we are able to reduce the original model to a model with only 30 states without incurring pronounced modelling errors. The Singular perturbation approximation balanced truncating technique is found to give the lowest modelling errors in low frequency ranges and hence is deemed most suitable for controller design and other real-time applications. (C) 2002 Elsevier Science Ltd. All rights reserved.

Linear matrix inequality-based robust model predictive control for time-delayed systems

This work addresses the solution to the problem of robust model predictive control (MPC) of systems with model uncertainty. The case of zone control of multi-variable stable systems with multiple time delays is considered. The usual approach of dealing with this kind of problem is through the inclusion of non-linear cost constraint in the control problem. The control action is then obtained at each sampling time as the solution to a non-linear programming (NLP) problem that for high-order systems can be computationally expensive. Here, the robust MPC problem is formulated as a linear matrix inequality problem that can be solved in real time with a fraction of the computer effort. The proposed approach is compared with the conventional robust MPC and tested through the simulation of a reactor system of the process industry.

Robust nonlinear model identification methods using forward regression

In this correspondence new robust nonlinear model construction algorithms for a large class of linear-in-the-parameters models are introduced to enhance model robustness via combined parameter regularization and new robust structural selective criteria. In parallel to parameter regularization, we use two classes of robust model selection criteria based on either experimental design criteria that optimizes model adequacy, or the predicted residual sums of squares (PRESS) statistic that optimizes model generalization capability, respectively. Three robust identification algorithms are introduced, i.e., combined A- and D-optimality with regularized orthogonal least squares algorithm, respectively; and combined PRESS statistic with regularized orthogonal least squares algorithm. A common characteristic of these algorithms is that the inherent computation efficiency associated with the orthogonalization scheme in orthogonal least squares or regularized orthogonal least squares has been extended such that the new algorithms are computationally efficient. Numerical examples are included to demonstrate effectiveness of the algorithms.

Robust linear mixed models with normal/independent distributions and Bayesian MCMC implementation

Linear mixed effects models have been widely used in analysis of data where responses are clustered around some random effects, so it is not reasonable to assume independence between observations in the same cluster. In most biological applications, it is assumed that the distributions of the random effects and of the residuals are Gaussian. This makes inferences vulnerable to the presence of outliers. Here, linear mixed effects models with normal/independent residual distributions for robust inferences are described. Specific distributions examined include univariate and multivariate versions of the Student-t, the slash and the contaminated normal. A Bayesian framework is adopted and Markov chain Monte Carlo is used to carry out the posterior analysis. The procedures are illustrated using birth weight data on rats in a texicological experiment. Results from the Gaussian and robust models are contrasted, and it is shown how the implementation can be used for outlier detection. The thick-tailed distributions provide an appealing robust alternative to the Gaussian process in linear mixed models, and they are easily implemented using data augmentation and MCMC techniques.

Multivariate analyses of UV-Vis absorption spectral data from cachaca wood extracts: a model to classify aged Brazilian cachacas according to the wood species used

Multivariate analyses of UV-Vis spectral data from cachaca wood extracts provide a simple and robust model to classify aged Brazilian cachacas according to the wood species used in the maturation barrels. The model is based on inspection of 93 extracts of oak and different Brazilian wood species by a non-aged cachaca used as an extraction solvent. Application of PCA (Principal Components Analysis) and HCA (Hierarchical Cluster Analysis) leads to identification of 6 clusters of cachaca wood extracts (amburana, amendoim, balsamo, castanheira, jatoba, and oak). LDA (Linear Discriminant Analysis) affords classification of 10 different wood species used in the cachaca extracts (amburana, amendoim, balsamo, cabreuva-parda, canela-sassafras, castanheira, jatoba, jequitiba-rosa, louro-canela, and oak) with an accuracy ranging from 80% (amendoim and castanheira) to 100% (balsamo and jequitiba-rosa). The methodology provides a low-cost alternative to methods based on liquid chromatography and mass spectrometry to classify cachacas aged in barrels that are composed of different wood species.

The kinesin walk: a dynamic model with elastically coupled heads.

Recently individual two-headed kinesin molecules have been studied in in vitro motility assays revealing a number of their peculiar transport properties. In this paper we propose a simple and robust model for the kinesin stepping process with elastically coupled Brownian heads that show all of these properties. The analytic and numerical treatment of our model results in a very good fit to the experimental data and practically has no free parameters. Changing the values of the parameters in the restricted range allowed by the related experimental estimates has almost no effect on the shape of the curves and results mainly in a variation of the zero load velocity that can be directly fitted to the measured data. In addition, the model is consistent with the measured pathway of the kinesin ATPase.

Robust Solutions of MultiObjective Linear Semi-Infinite Programs under Constraint Data Uncertainty

The multiobjective optimization model studied in this paper deals with simultaneous minimization of finitely many linear functions subject to an arbitrary number of uncertain linear constraints. We first provide a radius of robust feasibility guaranteeing the feasibility of the robust counterpart under affine data parametrization. We then establish dual characterizations of robust solutions of our model that are immunized against data uncertainty by way of characterizing corresponding solutions of robust counterpart of the model. Consequently, we present robust duality theorems relating the value of the robust model with the corresponding value of its dual problem.

Previsão de vento baseado em técnicas de Data Mining

Mestrado em Engenharia Electrotécnica – Sistemas Eléctricos de Energia

The daily and hourly energy consumption and load forecasting using artifitial neural network method: a case study using a set of 93 households in Portugal

It is important to understand and forecast a typical or a particularly household daily consumption in order to design and size suitable renewable energy systems and energy storage. In this research for Short Term Load Forecasting (STLF) it has been used Artificial Neural Networks (ANN) and, despite the consumption unpredictability, it has been shown the possibility to forecast the electricity consumption of a household with certainty. The ANNs are recognized to be a potential methodology for modeling hourly and daily energy consumption and load forecasting. Input variables such as apartment area, numbers of occupants, electrical appliance consumption and Boolean inputs as hourly meter system were considered. Furthermore, the investigation carried out aims to define an ANN architecture and a training algorithm in order to achieve a robust model to be used in forecasting energy consumption in a typical household. It was observed that a feed-forward ANN and the Levenberg-Marquardt algorithm provided a good performance. For this research it was used a database with consumption records, logged in 93 real households, in Lisbon, Portugal, between February 2000 and July 2001, including both weekdays and weekend. The results show that the ANN approach provides a reliable model for forecasting household electric energy consumption and load profile. © 2014 The Author.

Monitoring the ex-vivo expansion of human mesenchymal stem/stromal cells in xeno-free microcarrier-based reactor systems by MIR spectroscopy

Human mesenchymal stem/stromal cells (MSCs) have received considerable attention in the field of cell-based therapies due to their high differentiation potential and ability to modulate immune responses. However, since these cells can only be isolated in very low quantities, successful realization of these therapies requires MSCs ex-vivo expansion to achieve relevant cell doses. The metabolic activity is one of the parameters often monitored during MSCs cultivation by using expensive multi-analytical methods, some of them time-consuming. The present work evaluates the use of mid-infrared (MIR) spectroscopy, through rapid and economic high-throughput analyses associated to multivariate data analysis, to monitor three different MSCs cultivation runs conducted in spinner flasks, under xeno-free culture conditions, which differ in the type of microcarriers used and the culture feeding strategy applied. After evaluating diverse spectral preprocessing techniques, the optimized partial least square (PLS) regression models based on the MIR spectra to estimate the glucose, lactate and ammonia concentrations yielded high coefficients of determination (R2 ≥ 0.98, ≥0.98, and ≥0.94, respectively) and low prediction errors (RMSECV ≤ 4.7%, ≤4.4% and ≤5.7%, respectively). Besides PLS models valid for specific expansion protocols, a robust model simultaneously valid for the three processes was also built for predicting glucose, lactate and ammonia, yielding a R2 of 0.95, 0.97 and 0.86, and a RMSECV of 0.33, 0.57, and 0.09 mM, respectively. Therefore, MIR spectroscopy combined with multivariate data analysis represents a promising tool for both optimization and control of MSCs expansion processes.

Planeamento de redes de distribuição de energia

O planeamento de redes de distribuição tem como objetivo assegurar a existência de capacidade nas redes para a fornecimento de energia elétrica com bons níveis de qualidade de serviço tendo em conta os fatores económicos associados. No âmbito do trabalho apresentado na presente dissertação, foi elaborado um modelo de planeamento que determina a configuração de rede resultante da minimização de custos associados a: 1) perdas por efeito de joule; 2) investimento em novos componentes; 3) energia não entregue. A incerteza associada ao valor do consumo de cada carga é modelada através de lógica difusa. O problema de otimização definido é resolvido pelo método de decomposição de benders que contempla dois trânsitos de potências ótimos (modelo DC e modelo AC) no problema mestre e escravo respectivamente para validação de restrições. Foram também definidos critérios de paragem do método de decomposição de benders. O modelo proposto classifica-se como programação não linear inteira mista e foi implementado na ferramenta de otimização General Algebraic Modeling System (GAMS). O modelo desenvolvido tem em conta todos componentes das redes para a otimização do planeamento, conforme podemos analisar nos casos de estudo implementados. Cada caso de estudo é definido pela variação da importância que cada uma das variáveis do problema toma, tendo em vista cobrir de alguma todos os cenários de operação expetáveis. Através destes casos de estudo verifica-se as várias configurações que a rede pode tomar, tendo em conta as importâncias atribuídas a cada uma das variáveis, bem como os respetivos custos associados a cada solução. Este trabalho oferece um considerável contributo no âmbito do planeamento de redes de distribuição, pois comporta diferentes variáveis para a execução do mesmo. É também um modelo bastante robusto não perdendo o ‘norte’ no encontro de solução para redes de grande dimensão, com maior número de componentes.

Canine distemper virus : persistence and pathology in the central nervous system

Résumé : Le virus de la maladie de Carré (en anglais: canine distemper virus, CDV) qui est pathogène pour les chiens et autres carnivores, est très semblable au virus de la rougeole humaine (en anglais MV). Ces deux virus font partie du genre des Morbillivirus qui appartient à la famille des Paramyxoviridae. Ils induisent des complications dans le système nerveux central (SNC). Au stade précoce et aigu de l'infection du SNC, le CDV induit une démyélinisation (1). Ce stade évolue dans certains cas vers une infection chronique avec progression de la démyélinisation. Pendant le stade précoce, qui suit en général de trois semaines les premiers symptômes, le processus de démyélinisation est associé à la réplication du virus et n'est pas considéré comme inflammatoire (1). Par contre, au stade chronique, la progression des plaques de démyélinisation semble être plutôt liée à des processus immunogènes caractéristiques (2), retrouvés également dans la sclérose en plaques (SEP) chez les humains. Pour cette raison, le CDV est considéré comme un modèle pour la SEP humaine et aussi pour l'étude des maladies et complications induites par les Morbillivirus en général (3). Dans notre laboratoire, nous avons utilisé la souche A75/17-CDV, qui est considérée comme le modèle des souches neurovirulentes de CDV. Nous avons cherché en premier lieu à établir un système robuste pour infecter des cultures neuronales avec le CDV. Nous avons choisi les cultures primaires de l'hippocampe du nouveau-né de rat (4), que nous avons ensuite infecté avec une version modifiée du A75/17, appelée rgA75/17-V (5). Dans ces cultures, nous avons prouvé que le CDV infecte des neurones et des astrocytes. Malgré une infection qui se diffuse lentement entre les cellules, cette infection cause une mort massive aussi bien des neurones infectés que non infectés. En parallèle, les astrocytes perdent leur morphologie de type étoilé pour un type polygonal. Finalment, nous avons trouvé une augmentation importante de la concentration en glutamate dans le milieu de culture, qui laisse présumer une sécrétion de glutamate par les cultures infectées (6). Nous avons ensuite étudié le mécanisme des effets cytopathiques induits par le CDV. Nous avons d'abord démontré que les glycoprotéines de surface F et H du CDV s'accumulent massivement dans le réticulum endoplasmique (RE). Cette accumulation déclenche un stress du RE, qui est caractérisé par une forte expression du facteur de transcription proapoptotique CHOP/GADD 153 et de le la calreticuline (CRT). La CRT est une protéine chaperonne localisée dans le RE et impliquée dans l'homéostasie du calcium (Ca2+) et dans le repliement des protéines. En transfectant des cellules de Vero avec des plasmides codant pour plusieurs mutants de la glycoprotéine F de CDV, nous avons démontré une corrélation entre l'accumulation des protéines virales dans le RE et l'augmentation de l'expression de CRT, le stress du RE et la perte de l'homéostasie du Ca2+. Nous avons obtenu des résultats semblables avec des cultures de cellules primaires de cerveau de rat. Ces résultats suggèrent que la CRT joue un rôle crucial dans les phénomènes neurodégénératifs pendant l'infection du SNC, notamment par le relazgage du glutamate via le Ca2+. De manière intéressante, nous démontrons également que l'infection de CDV induit une fragmentation atypique de la CRT. Cette fragmentation induit une re-localisation et une exposition sélective de fragments amino-terminaux de la CRT, connus pour êtres fortement immunogènes à la surface des cellules infectées et non infectées. A partir de ce résultat et des résultats précédents, nous proposons le mécanisme suivant: après l'infection par le CDV, la rétention dans le RE des protéines F et H provoque un stress du RE et une perte de l'homéostasie du Ca2+. Ceci induit la libération du glutamate, qui cause une dégénération rapide du SNC (sur plusieurs jours ou semaines) correspondant à la phase aiguë de la maladie chez le chien. En revanche, les fragments amino-terminaux de la CRT libérés à la surface des cellules infectées peuvent avoir un rôle important dans l'établissement d'une démyélinisation d'origine immunogène, typique de la phase chronique de l'infection de CDV. Summary : The dog pathogen canine distemper virus (CDV), closely related to the human pathogen measles virus (MV), belongs to the Morbillivirus genus of the Paramyxoviridae family. Both CDV and NIV induce complications in the central nervous system (CNS). In the acute early stage of the infection in CNS, the CDV infection induces demyelination. This stage is sometimes followed by a late persistent stage of infection with a progression of the demyelinating lesions (1). The acute early stage occurs around three weeks after the infection and demyelinating processes are associated with active virus replication and are not associated to inflammation (1). In contrast during late persistent stage, the demyelination plaque progression seems to be mainly due to an immunopathological process (2), which characteristics are shared in many aspects with the human disease multiple sclerosis (MS). For these reasons, CDV is considered as a model for human multiple sclerosis, as well as for the study of Morbillivirus-mediated pathogenesis (3). In our laboratory, we used the A75/17-CDV strain that is considered to be the prototype of neurovirulent CDV strain. We first sought to establish a well characterized and robust model for CDV infection of a neuronal culture. We chose primary cultures from newborn rat hippocampes (4) that we infected with a modified version of A75/17, called rgA75/17-V (5). In these cultures, we showed that CDV infects both neurons and astrocytes. While the infection spreads only slowly to neighbouring cells, it causes a massive death of neurons, which includes also non-infected neurons. In parallel, astrocytes undergo morphological changes from the stellate type to the polygonal type. The pharmacological blocking of the glutamate receptors revealed an implication of glutamatergic signalling in the virus-mediated cytopathic effect. Finally, we found a drastic increase concentration of glutamate in the culture medium, suggesting that glutamate was released from the cultured cells (6). We further studied the mechanism of the CDV-induced cytopathic effects. We first demonstrated that the CDV surface glycoprotein F and H markedly accumulate in the endoplasmic reticulum (ER). This accumulation triggers an ER stress, which is characterized by increased expression of the proapoptotic transcription factor CHOP/GADD 153 and calreticulin (CRT). CRT is an ER resident chaperon involved in the Ca2+ homeostasis and in the response to misfolded proteins. Transfections of Vero cells with plasmids encoding various CDV glycoprotein mutants reveal a correlation between accumulation of viral proteins in the ER, CRT overexpression, ER stress and alteration of ER Ca2+ homeostasis. Importantly, similar results are also obtained in primary cell cultures from rat brain. These results suggest that CRT plays a crucial role in CNS infection, particularly due to CRT involvement in Ca2+ mediated glutamate releases, and subsequent neurodegenerative disorders. Very intriguingly, we also demonstrated that CDV infection induces an atypical CRT fragmentation, with relocalisation and selective exposure of the highly immunogenic CRT N-terminal fragments at the surface of infected and neighbouring non-infected cells. Altogether our results combined with previous findings suggest the following scenario. After CDV infection, F and H retention alter Ca2+ homeostasis, and induce glutamate release, which in turn causes rapid CNS degeneration (within days or a week) corresponding to the acute phase of the disease in dogs. In contrast, the CRT N-terminal fragments released at the surface of infected cells may rather have an important role in the establishment of the autoimmune demyelination in the late stage of CDV infection.

Cooperative kinking at distant sites in mechanically stressed DNA.

In cells, DNA is routinely subjected to significant levels of bending and twisting. In some cases, such as under physiological levels of supercoiling, DNA can be so highly strained, that it transitions into non-canonical structural conformations that are capable of relieving mechanical stress within the template. DNA minicircles offer a robust model system to study stress-induced DNA structures. Using DNA minicircles on the order of 100 bp in size, we have been able to control the bending and torsional stresses within a looped DNA construct. Through a combination of cryo-EM image reconstructions, Bal31 sensitivity assays and Brownian dynamics simulations, we have been able to analyze the effects of biologically relevant underwinding-induced kinks in DNA on the overall shape of DNA minicircles. Our results indicate that strongly underwound DNA minicircles, which mimic the physical behavior of small regulatory DNA loops, minimize their free energy by undergoing sequential, cooperative kinking at two sites that are located about 180° apart along the periphery of the minicircle. This novel form of structural cooperativity in DNA demonstrates that bending strain can localize hyperflexible kinks within the DNA template, which in turn reduces the energetic cost to tightly loop DNA.