This paper presents an efficient computer control technique for regulation of anesthesia in humans. The anesthetic used is propofol and the objective is to control the degree of hypnosis of the patient. The paper describes the basic hardware/software setup of the system and the closed-loop methodologies. The bispectral index (BIS) is considered as the feedback signal. The control methods proposed here are based in the use of proportional integral controllers with dead-time compensation to avoid undesirable oscillations in the BIS signal during the process. The compensation is based on the Smith predictor. To guarantee the applicability of the method to different patients, an adaptive module to tune the compensator is developed. Some real and simulated results are presented in this work to attest the efficiency of the methods used.

ResumenEste artículo se centra en el modelado y control de la hipnosis durante la anestesia de pacientes sometidos a intervenciones quirúrgicas. Por un lado en este trabajo se aborda el problema del modelado del proceso presentando resultados validados con pacientes reales. Asimismo, se propone un controlador avanzado para regular el estado hipnótico. El algoritmo empleado se basa en combinar una acción nominal obtenida a partir de la dinámica inversa juntamente con una acción correctora que se obtiene a partir de un controlador predictivo. El trabajo persigue desarrollar una técnica que permita la regulación del estado del paciente y que tenga características de adaptabilidad a los diferentes individuos. Se muestran resultados preliminares de la estrategia propuesta para demostrar la eficiencia del sistema

Currently, there is great interest in reducing the consumption of fossil fuels (and other non-renewable energy sources) in order to preserve the environment; smart buildings are commonly proposed for this purpose as they are capable of producing their own energy and using it optimally. However, at times, solar energy is not able to supply the energy demand fully; it is mandatory to know the quantity of energy needed to optimize the system. This research focuses on the prediction of output temperature from a solar thermal collector. The aim is to measure solar thermal energy and optimize the energy system of a house (or building). The dataset used in this research has been taken from a real installation in a bio-climate house located on the Sotavento Experimental Wind Farm, in north-west Spain. A hybrid intelligent model has been developed by combining clustering and regression methods such as neural networks, polynomial regression, and support vector machines. The main findings show that, by dividing the dataset into small clusters on the basis of similarity in behavior, it is possible to create more accurate models. Moreover, combining different regression methods for each cluster provides better results than when a global model of the whole dataset is used. In temperature prediction, mean absolute error was lower than 4 ${}^{\circ }$ C.