This work proposes a new and simple design for the filtered Smith predictor (FSP), which belongs to a class of dead-time compensators (DTCs) and allows the handling of stable, unstable, and integrative processes. For this purpose, first, to use lower-order controller and filters, it is shown that it is not necessary to use the integral action in the primary controller, which is used to tune the set-point response; then, the FSP filters are designed to obtain the desired disturbance rejection, robustness, and noise attenuation. Using this procedure, it is possible to obtain a better compromise between performance and complexity than other solutions in the literature. Two simulation case studies are used to compare the obtained solution with some recently published results. A practical experiment involving a neonatal intensive care unit is also presented to illustrate the usefulness of the proposed DTC.

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.

This paper seeks to estimate through Artificial Neural Networks the future behavior of temperature and humidity inside an incubator. This goal is motivated by the observation that the model-based predictive control is an interesting alternative for the generation of control signals of a neonatal incubator since: (i) it seeks to optimize a performance criterion that considers the future behavior of this controller, and (ii) restrictions may be imposed on future control signals. These two features can make more safe and comfortable the microclimate inside the device for the newborn: variables such as temperature and humidity can be better kept within the limits of technical standards such as the NBR IEC 601-2-19 and its amendment No. 1, NBR IEC 60601-2-19-2000. However, one predictive model of the process to be controlled must first be obtained. The obtained neural model has accuracy in predicting the incubator behavior one time step forward compatible with the technical standard, and it is ready to be applied in a predictive control structure.