Modelling the human cardiorespiratory system using computer simulation tools can serve to help physicians to comprehend the causes and development of cardiorespiratory diseases. The objective of this paper is to develop an integrated model of the cardiovascular and respiratory systems, along with their intrinsic control mechanisms, by combining analogous hydraulic-electric and diffusion-electric circuits, respectively. This modelling task is performed in object-oriented language in SIMSCAPE using the physical interconnected components to define the underlying dynamic equations. Simulation steady state results under rest and under variable physical exercise conditions, as well as under limiting conditions show a high qualitative agreement with clinical observations reported in literature. This object-oriented modelling approach, based on the combined use of electrical analogies, proves to be avaluable tool as a test bench for different strategies aimed to qualitative prediction of the effects of cardiorespiratory interactions during exercise, thus avoiding the formulation of complex mathematical models.

Background and objectiveThe understanding of complex biological systems performance is one of the key issues in physiology, and several computational methods based on computer simulation have been applied to determine the behaviour of nonlinear systems. System Dynamics is an intuitive modelling methodology based on qualitative reasoning, whereby a conceptual physiological model can be described as a set of cause–effect relationships between the physiological variables of a system, so that a set of dynamic equations describing the system behaviour quantitatively can be derived.MethodsThis paper presents system dynamics modelling methodology and its application for short-term arterial pressure control exerted through the baroreceptor reflex over a multi-compartmental cardiovascular model under the OpenModelica object-oriented simulation environment.ResultsThe performance of the controlled system is analysed by simulation in light of the existing hypothesis and validation tests previously performed, demonstrating the effectiveness of the short-term regulation mechanism under physiological and pathological conditions.ConclusionsThe system dynamics can be viewed as a powerful and easy-to-use educational tool and useful in Health Sciences so as to explain the behaviour of a physiologic system under study.

This paper proposes a randomized algorithm for feasibility of uncertain LMIs. The algorithm is based on the solution of a sequence of semidefinite optimization problems involving a reduced number of constraints. A bound of the maximum number of iterations required by the algorithm is given. Finally, the performance and behaviour of the algorithm are illustrated by means of a numerical example.

In this paper, we present a randomized strategy for design under uncertainty. The main contribution is to provide a general class of sequential algorithms which satisfy the required specifications using probabilistic validation. At each iteration of the sequential algorithm, a candidate solution is probabilistically validated by means of a set of randomly generated uncertainty samples. The idea of validation sets has been used in some randomized algorithms when a given candidate solution is classified as probabilistic solution when it satisfies all the constraints on the validation set. In this paper, we show the limitations of this strategy and present a more general setting where the candidate solution may violate the specifications for a reduced number of elements of the validation set. This generalized scheme exhibits some advantages, in particular in terms of obtaining a probabilistic solution.

In this paper we show how randomized algorithms can be applied to the design of a robust controller.

The production of hot water for use in a Hospital can use solar energy as a main energy source. The hot water production system can also use fossil energy in cases were the demand is too high to be satisfied with solar power. The resulting system is hybrid and its control poses some problems. In this paper the work in progress towards the control of this kind of hybrid system is shown. The model used belongs to a general category of models that can be applied to various types of renewable energy plants. The abstract representation receives the name of RESCUE model. In this paper it is applied to a particular hot water system installed at the Virgen del Rocío hospital in Seville (Spain). The RESCUE model allows to identify niches of inefficiency in the operation. The model is a first step towards the improving of operation by means of adequate controllers.

A family of models that can be applied to various types of renewable energy plants is proposed. The methodology is used to model a solar plant for the production of sanitary water (the hot water production system installed at the “Hospital Universitario Virgen del Rocío”, Seville, Spain). A detailed examination of the behavior of the plant has produced a model which has served to identify niches of inefficiency in the operation. The model is later used to tune the parameters of a controller to improve operation.

In this paper, we study the sample complexity of probabilistic methods for control of uncertain systems. In particular, we show the role of the binomial distribution for some problems involving analysis and design of robust controllers with finite families. We also address the particular case in which the design problem can be formulated as an uncertain convex optimization problem. The results of the paper provide simple explicit sample bounds to guarantee that the obtained solutions meet some pre-specified probabilistic specifications.