As Redes Elétricas Inteligentes e o Futuro dos Sistemas de Energia Elétrica"; Cenário atual. Definições e conceitos básicos de redes elétricas inteligentes. Tecnologias viabilizadoras: medição inteligente, tecnologias de informação e comunicação, sincrofasores, geração distribuída, etc. Aplicações em redes de distribuição: auto restabelecimento, controle de tensão, gerenciamento de perdas, etc. Aplicações no sistema interligado: redes de PMUs, WAMPAC, etc. Situação no Brasil e no mundo. Visão de futuro.

Neste mini-curso, busca-se evidenciar a importância da consideração da validade regional dos modelos fuzzy de tipo Takagi-Sugeno (T-S) para o desenvolvimento de ferramentas de análise e síntese de sistemas de controle não lineares, e discutir problemas associados à complexidade desses modelos dos pontos de vista de implementação numérica dos algoritmos e de implementação computacional dos controladores. Serão considerados sistemas não lineares discretos no tempo, inicialmente sem a presença de atrasos e, a seguir, sistemas com atrasos nos estados. Modelos fuzzy T-S têm sido investigados no decorrer das últimas décadas principalmente por propiciarem o desenvolvimento de metodologias que possuem caráter sistemático para análise e projeto de sistemas de controle não lineares, além de propiciar soluções numéricas via técnicas baseadas em desigualdades matriciais lineares (LMIs, do inglês linear matrix inequalities). Embora diversos resultados sejam encontrados na literatura, existem questões de motivação prática que devem também ser consideradas no contexto do controle fuzzy baseado em modelo (FMB, do inglês Fuzzy Model Based). Primeiramente, as técnicas de modelagem fuzzy T-S garantem a convexidade do modelo e/ou a sua precisão de representação somente para uma determinada região do espaço de estados. Portanto, para estratégias de controle que dependem da propriedades de convexidade, a estabilidade do sistema de malha fechada formado pelo sistema não linear realimentado pela lei de controle fuzzy deve ser estudada no contexto de estabilidade local. Assim, é fundamental a determinação de regiões de estabilidade para o sistema de malha fechada. Essa importante característica dos modelos fuzzy T-S raramente é considerada na literatura, podendo implicar em perda de desempenho e até mesmo instabilidade do sistema em malha fechada. Outro problema inerente à utilização de modelos fuzzy T-S diz respeito ao aumento exponencial de complexidade do modelo com o número de não linearidades presentes no sistema, principalmente quando se busca descrever de forma exata a dinâmica do sistema a controlar. Isso implica no aumento da complexidade numérica dos algoritmos para análise e projeto, assim como no aumento da complexidade de implementação de leis de controle.

With the vision of the smart factory of the future, generally termed Industry 4.0, the manufacturing industries are currently undergoing a fundamental new orientation on the basis of the Cyber-Physical Systems and Internet of Things and Services paradigms. All parts along the manufacturing chain are nowadays equipped with embedded computing, communication and networking capabilities and are expected to interact in an optimal way towards the goal of an energy and resource efficient, save and reliable production process. Through decentralized optimal decision-making and an appropriate communication among the networked individual parts, the whole production process of the future is expected to operate optimally. In this presentation the challenges and opportunities of Industry 4.0 for the field of automatic control are discussed. We will in particular investigate the potential impact of networked and optimization-based control for the fourth industrial revolution and will argue that some new developments in the control field, especially connected to distributed and economic model predictive control, appear to be ideally suited to have a potential  impact in the new Industry 4.0 environment.

We have to face a demographic change in many countries: People are getting older and older and suffer from a whole variety of age-related diseases. The rates of hospitalisation increase.Clinical and home surveillance has to include age-specific morbidity and mortality. Now we have to answer the question, if it is possible to keep the aged people as long as possible at home to reduce costs but not the quality of services in medicine , care and general support. Here interdisciplinary engineering and the digital transformation can help and represent excellent opportunities to solve the above mentioned problems. New ecosystems can be formed and new disruptive models are necessary. Sensors, electronics, software merge in combination with business models. The presentation refers to digitally transformed processes and shows different applications on the field of body area networks and vital parameter measurement in the context of telemedical and clinical applications. Furthermore the demographic change and the increased demand for medical care associated hereto as well as increasing acute and chronic diseases will ensure a stable growth in demand. the same applies for other market determinants, for example the increasing use of self medication, which is increasingly replacing out-patient and hospital care, as well as the rising number of generic and biotechnological products. The demographic change became an issue for German local authorities, since it is evident, that more and more municipalities have no medical service any more. Here new models of automation based on point-of-care systems in combination with IOT-based services can help to face this medical and care service problem and moreover can even reach a better quality of medical care than ever before. THe talk will also refer to this situation and how it is possible to increase quality of different forms of care by sensor and IT-systems.

This talk describes our efforts to abstract from the animal visual system the computational principles to explain images in video. We develop a hierarchical, distributed architecture of dynamical systems that self-organizes to explain the input imagery using an empirical Bayes criterion with sparseness constraints and dual state estimation. The interpretation of the images is mediated through causes that flow top down and change the priors for the bottom up processing. We will present preliminary results in several data sets.

The ability to control the behavior of intracellular networks has relevant applications, ranging from an optimized synthesis of metabolites in bacteria to the cure of human diseases. To implement a controller in vivo, one may insert synthetic biomolecules into the cell being controlled. This seminar presents a formal approach, based on Supervisory Control Theory (SCT), to guide the design of synthetic genes that control the dynamics of gene regulatory networks, major players in cell dynamics, so that a set of specifications is achieved. Such a systematic procedure is important to make the control of large networks by synthetic molecules feasible. In this approach, it is assumed that the behavior of gene networks can be abstracted by finite state machines, where each state corresponds to a set of gene expression levels and the events are associated with the activation/repression of genes.