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1.
Biomed Tech (Berl) ; 60 Suppl 1: s238-69, 2015 Sep.
Article in English | MEDLINE | ID: mdl-26360475
3.
Med Biol Eng Comput ; 43(5): 589-98, 2005 Sep.
Article in English | MEDLINE | ID: mdl-16411631

ABSTRACT

Three different discrete controllers were designed and tuned to be used in conjunction with a rotary blood pump during cardiopulmonary heart-lung support. The controllers were designed to operate in both steady and pulsatile modes. The system and methods were tested in a circulatory haemodynamic simulator. To guarantee stable control of the non-linear circulatory system in the presence of patient parameter uncertainties, a proportional plus integral (PI) and an H infinity controller were robustly tuned, using a non-linear time-varying model. (H infinity refers to the Hardy space, the set of bounded functions, analytic in the right half plane. The H infinity controller is the solution to the H infinity norm optimisation problem.) A self-tuning general predictive controller (GPC), together with an adaptive Kalman filter (KF) estimator, was compared with the two robustly tuned controllers. The closed-loop blood flow control circuit was set up in simulation routines first. The blood flow controllers were validated in a circulatory hydrodynamic simulator (MOCK) combined with a rotary blood pump. Parameters of the system simulator were changed continuously, and the controllers were tested over a wide range of different operating points. Disturbances in the form of discontinuous additive parameter uncertainties were applied. The closed-loop systems remained robustly stable. The robustly tuned H infinity controller showed the best control performance, in contrast to the GPC controller, which was near instability in regions of strongly varying non-linear system gain. Compared with the H infinity controller, the PI controller showed slightly worse behaviour, but the closed-loop response was acceptable, even in regions of strongly varying non-linear system gain and during pulsatile perfusion. The rotary blood pump could provide stationary and pulsatile perfusion under control conditions. Controlled variables were hereby mean blood flow, pulsatility index and heart rate. All three controllers were developed for an arterial mean flow of 0-6 l min(-1) and a heart rate of up to 70 beats per minute. Pulsatile closed-loop perfusion could provide up to 30 mmHg pressure variation in the simulated ascending aorta at a mean flow of 3 l min(-1).


Subject(s)
Cardiopulmonary Bypass/methods , Models, Cardiovascular , Aorta/physiology , Cardiopulmonary Bypass/instrumentation , Hemodynamics , Humans , Nonlinear Dynamics , Pulsatile Flow
4.
Biomed Tech (Berl) ; 49(11): 316-21, 2004 Nov.
Article in English | MEDLINE | ID: mdl-15624869

ABSTRACT

Automatic control of the blood gas parameters during extracorporeal circulation has the potential to improve the quality of this procedure and to relieve the personnel from a time consuming task. This paper describes a model of the underlying system for a standard clinical set-up and pinpoints the major difficulties which are the variations of the process gains and the blood- and gas-flow dependent dead times and time constants. Scheduled PI-controllers both for the arterial oxygen as well as for the carbon dioxide partial pressure were designed. Scheduling was based on the blood flow rate. These controllers were tested in a simulation environment. The control systems remained stable under all tested operating condition, but if the blood flow rate was changed abruptly rather large load errors occurred. The performance was improved markedly by adding a feed-forward control path which directly influences the actuating signals based on the actual blood flow rate and the hemoglobin contents, variables which are measured anyway. The major conclusion of this study is to use such direct feed-forward compensation even if more sophisticated control algorithms are used.


Subject(s)
Carbon Dioxide/blood , Cardiopulmonary Bypass/instrumentation , Computer Simulation , Oxygen/blood , Signal Processing, Computer-Assisted/instrumentation , Algorithms , Blood Flow Velocity/physiology , Blood Gas Analysis/instrumentation , Computer Systems , Feedback/physiology , Humans , Oxygenators, Membrane
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