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Cardiovasc Eng Technol. 2019 Oct 30. doi: 10.1007/s13239-019-00440-3. [Epub ahead of print]

A New Approach Based on a Multiobjective Evolutionary Algorithm for Accurate Control of Flow Rate and Blood Pressure in Cardiac Bioreactors.

Author information

1
Département de Génie Mécanique, Université Laval, Québec, QC, G1V 0A6, Canada.
2
Département de Génie Mécanique, Université Laval, Québec, QC, G1V 0A6, Canada. Jean.Ruel@gmc.ulaval.ca.
3
Pavillon Adrien-Pouliot, local 1314-C, Québec, 412245, Canada. Jean.Ruel@gmc.ulaval.ca.

Abstract

PURPOSE:

Accurately reproducing physiological and time-varying variables in cardiac bioreactors is a difficult task for conventional control methods. This paper presents a new controller based on a genetic algorithm for the control of a cardiac bioreactor dedicated to the study and conditioning of heart valve substitutes.

METHODS:

A multi-objective genetic algorithm was designed to obtain an accurate simultaneous reproduction of physiological periodic time functions of the three most relevant variables characterizing the blood flow in the aortic valve. These three controlled variables are the flow rate and the pressures upstream and downstream of the aortic valve.

RESULTS:

Experimental results obtained with this new algorithm showed an accurate dynamic reproduction of these three controlled variables. Moreover, the controller can react and adapt continuously to changes happening over time in the cardiac bioreactor, which is a major advantage when working with living biological valve substitutes.

CONCLUSION:

The strong non-linear interaction that exists between the three controlled variables makes it difficult to obtain a precise control of any of these, let alone all three simultaneously. However, the results showed that this new control algorithm can efficiently overcome such difficulties. In the particular field of bioreactors reproducing the cardiovascular environment, such a flexible, versatile and accurate reproduction of these three interdependent controlled variables is unprecedented.

KEYWORDS:

Bioreactor; Genetic algorithm; Multi-objective evolutionary algorithm; Optimization

PMID:
31667784
DOI:
10.1007/s13239-019-00440-3

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