Measurement and diagnostic systems based on electronic sensors have been increasingly essential in the standardization of hospital equipment. The technical standard IEC (International Electrotechnical Commission) 60601-2-19 establishes requirements for neonatal incubators and specifies the calibration procedure and validation tests for such devices using sensors systems. This paper proposes a new procedure based on an inferential neural network to evaluate and calibrate a neonatal incubator. The proposal presents significant advantages over the standard calibration process, i.e., the number of sensors is drastically reduced, and it runs with the incubator under operation. Since the sensors used in the new calibration process are already installed in the commercial incubator, no additional hardware is necessary; and the calibration necessity can be diagnosed in real time without the presence of technical professionals in the neonatal intensive care unit (NICU). Experimental tests involving the aforementioned calibration system are carried out in a commercial incubator in order to validate the proposal.

Resumo -Este artigo propõe uma estratégia de controle preditivo baseado em modelo aplicado a um conversor boost com célula de comutação de três estados que confere mais simplicidade e sistematização nas fases de projeto e análise do controlador, cujo ganho integral ajustável dispensa o reprojeto nas matrizes de ponderação. Para simplificar a análise de estabilidade do controlador, utiliza-se o conceito de elipsoides de estabilidade, um assunto ainda pouco explorado neste contexto. O controle preditivo proposto parte da modelagem da planta no espaço de estados médio linear e variante no tempo, cujas variações paramétricas são tratadas como incertezas politópicas expressas por meio de desigualdades matriciais lineares (LMIs) com relaxações.Aspectos teórico-experimentais são aplicados e analisados em um conversor de 1 kW com incertezas na tensão de entrada e na carga. Além disso, para estabelecer uma base de desempenho, o MPC proposto é comparado com o controlador LQR clássico conhecido na literatura. A estratégia de controle proposta apresenta vantagens considerando as variações do modelo decorrente dos testes de cargas em aplicações de conversores estáticos CC-CC. Palavras-chave -Conversor ROBUST MPC-LMI CONTROLLER APPLIED TO THREE STATE SWITCHING CELL BOOST CONVERTERAbstract -This paper proposes a Model Predictive Control (MPC) strategy applied to Three State Switching Cell boost converter which leads more simplicity to the design steps and analysis to the controller, whose adjustable integral gain does not need the redesign of weighting matrices. To simplify the controller analysis, the ellipsoid stability concepts are used, a field few explored in this context. The proposed MPC starts from of Linear Time Varying(LTV) state space plant modeling whose parametric variables are modeled as politopic uncertainties via linear matrix inequalities (LMIs) approach with relaxations.Theoretical and experimental aspects are applied to 1 kW boost converter with voltage input and load uncertainties. Morevover, to lead the performance testing, the proposed MPC is compared with the classical LQR known in the literature.The proposed control strategy presents advantages considering the model variations due to load testing in DC-DC converters applications. Keywords -

This paper proposes a robust multivariable predictive control algorithm that improves the robustness of closed loop systems, even when they have multiple time delays between the inputs and outputs. The desired robustness is achieved by including an appropriate filter on the disturbances model. The proposed algorithm is applied to the control of humidity and temperature of a neonatal incubator. Simulation and experimental results show the advantages of the proposed algorithm compared to others proposed in the literature.

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The growth of fuzzy logic applications led to the need of finding efficient ways to implement them. The FPGAs (Field Programmable Gate Arrays) are reconfigurable logic devices that provide mainly practicality and portability, with low consumption of energy, high speedy of operation and large capacity of data storage. These characteristics, combined with the ability of synthesizing circuits, make FPGAs powerful tools for project development and prototyping of digital controllers. In this paper, the implementation of a Mamdani Fuzzy Inference System has been demonstrated using VHDL programming language. The accuracy of the model on FPGA was compared with simulation results obtained using MATLAB & Fuzzy Logic Tool Box.