scholarly journals Development and In Silico Evaluation of a Model-Based Closed-Loop Fluid Resuscitation Control Algorithm

2019 ◽  
Vol 66 (7) ◽  
pp. 1905-1914 ◽  
Author(s):  
Xin Jin ◽  
Ramin Bighamian ◽  
Jin-Oh Hahn
Keyword(s):  
Fluid Resuscitation ◽  
In Silico ◽  
Control Algorithm ◽  
Closed Loop ◽  
Model Based

scholarly journals Quasi In-Silico Validations of a Nonlinear LPV Model-based Robust Glucose Control Algorithm for Type I Diabetes

2011 ◽  
Vol 44 (1) ◽  
pp. 7114-7119 ◽  
Author(s):  
Levente Kovács ◽  
Péter Szalay ◽  
Zsuzsanna Almássy ◽  
Zoltán Benyo ◽  
László Barkai
Keyword(s):  
Glucose Control ◽  
In Silico ◽  
Control Algorithm ◽  
Type I Diabetes ◽  
Type I ◽  
Model Based

scholarly journals Closed-Loop Fluid Resuscitation Control Via Blood Volume Estimation

2016 ◽  
Vol 138 (11) ◽  
Author(s):  
Ramin Bighamian ◽  
Chang-Sei Kim ◽  
Andrew T. Reisner ◽  
Jin-Oh Hahn
Keyword(s):  
Blood Volume ◽  
Fluid Resuscitation ◽  
Closed Loop ◽  
Volume Estimation ◽  
Closed Loop Control ◽  
Loop Control ◽  
Model Based ◽  
Arterial Blood ◽  
Wide Range ◽  
Electrocardiogram Ecg

This paper presents a closed-loop control of fluid resuscitation to overcome hypovolemia based on model-based estimation of relative changes in blood volume (BV). In this approach, the control system consists of a model-based relative BV (RBV) estimator and a feedback controller. The former predicts relative changes in the BV response to augmented fluid by analyzing an arterial blood pressure (BP) waveform and the electrocardiogram (ECG). Then, the latter determines the amount of fluid to be augmented by comparing target versus predicted relative changes in BV. In this way, unlike many previous methods for fluid resuscitation based on controlled variable(s) nonlinearly correlated with the changes in BV, fluid resuscitation can be guided by a controlled variable linearly correlated with the changes in BV. This paper reports initial design of the closed-loop fluid resuscitation system and its in silico evaluation in a wide range of hypovolemic scenarios. The results suggest that closed-loop fluid resuscitation guided by a controlled variable linearly correlated with the changes in BV can be effective in overcoming hypovolemia: across 100 randomly produced hypovolemia cases, it resulted in the BV regulation error of 7.98 ± 171.6 ml, amounting to 0.18 ± 3.04% of the underlying BV. When guided by pulse pressure (PP), a classical controlled variable nonlinearly correlated with the changes in BV; the same closed-loop fluid resuscitation system resulted in persistent under-resuscitation with the BV regulation error of −779.1 ± 147.4 ml, amounting to −13.9 ± 2.65% of the underlying BV.

Closed-Loop Control of Fluid Resuscitation via Blood Volume Estimation

2014 ◽  
Author(s):  
Ramin Bighamian ◽  
Andrew T. Reisner ◽  
Jin-Oh Hahn
Keyword(s):  
Control System ◽  
Blood Volume ◽  
Fluid Resuscitation ◽  
Closed Loop ◽  
Closed Loop Control ◽  
Loop Control ◽  
Model Based ◽  
Closed Loop Control System ◽  
Volume Response ◽  
Loop Control System

This paper presents a model-based approach to the closed-loop control of fluid resuscitation against hypovolemia. In this approach, the control system consists of a model-based blood volume estimator and a feedback controller. The model-based blood volume estimator derives relative changes in the blood volume response to the augmented fluid by analyzing an arterial blood pressure waveform and the electrocardiogram. Then, the feedback controller determines the amount of fluid to be augmented by comparing targeted versus estimated relative changes in the blood volume. In this way, unlike many previous methods for fluid resuscitation based on indirect surrogate(s) of blood volume, fluid resuscitation can be directly guided by the blood volume response. This paper reports initial design of the closed-loop control system and its simulation-based evaluation in a wide range of hypovolemic and physiologic scenarios. The results suggest that the proposed closed-loop control system is very effective in resuscitation against hypovolemia: in 97 out of 100 simulated hypovolemia, the final blood volume achieved by the control system was within 10% of its optimal value.

A Digital Algorithm for Near-Minimum-Time Control of Robot Manipulators

1987 ◽  
Vol 109 (4) ◽  
pp. 320-327 ◽  
Author(s):  
C. K. Kao ◽  
A. Sinha ◽  
A. K. Mahalanabis
Keyword(s):  
Control Algorithm ◽  
State Feedback ◽  
Closed Loop ◽  
Robot Manipulator ◽  
Minimum Time ◽  
State Feedback Control ◽  
Final States ◽  
Closed Loop System ◽  
Time Control ◽  
Digital Algorithm

A digital state feedback control algorithm has been developed to obtain the near-minimum-time trajectory for the end-effector of a robot manipulator. In this algorithm, the poles of the linearized closed loop system are judiciously placed in the Z-plane to permit near-minimum-time response without violating the constraints on the actuator torques. The validity of this algorithm has been established using numerical simulations. A three-link manipulator is chosen for this purpose and the results are discussed for three different combinations of initial and final states.

scholarly journals Model-Based Reinforcement Learning for Closed-Loop Dynamic Control of Soft Robotic Manipulators

2019 ◽  
Vol 35 (1) ◽  
pp. 124-134 ◽  
Author(s):  
Thomas George Thuruthel ◽  
Egidio Falotico ◽  
Federico Renda ◽  
Cecilia Laschi
Keyword(s):  
Reinforcement Learning ◽  
Closed Loop ◽  
Dynamic Control ◽  
Robotic Manipulators ◽  
Model Based

scholarly journals Closed-loop vasopressor control: in-silico study of robustness against pharmacodynamic variability

2018 ◽  
Vol 33 (5) ◽  
pp. 795-802 ◽  
Author(s):  
Joseph Rinehart ◽  
Alexandre Joosten ◽  
Michael Ma ◽  
Michael-David Calderon ◽  
Maxime Cannesson
Keyword(s):  
In Silico ◽  
Closed Loop ◽  
In Silico Study

scholarly journals Stabilization of the Magnetic Levitation System

2021 ◽  
Vol 11 (21) ◽  
pp. 10369
Author(s):  
Štefan Chamraz ◽  
Mikuláš Huba ◽  
Katarína Žáková
Keyword(s):  
Closed Loop ◽  
Magnetic Levitation ◽  
Internal Model Control ◽  
Pass Filter ◽  
Low Pass Filter ◽  
First Order ◽  
Model Based ◽  
Model Control ◽  
Levitation System ◽  
Magnetic Levitation System

This paper contributes toward research on the control of the magnetic levitation plant, representing a typical nonlinear unstable system that can be controlled by various methods. This paper shows two various approaches to the solution of the controller design based on different closed loop requirements. Starting from a known unstable linear plant model—the first method is based on the two-step procedure. In the first step, the transfer function of the controlled system is modified to get a stable non-oscillatory system. In the next step, the required first-order dynamic is defined and a model-based PI controller is proposed. The closed loop time constant of this first-order model-based approach can then be used as a tuning parameter. The second set of methods is based on a simplified ultra-local linear approximation of the plant dynamics by the double-integrator plus dead-time (DIPDT) model. Similar to the first method, one possible solution is to stabilize the system by a PD controller combined with a low-pass filter. To eliminate the offset, the stabilized system is supplemented by a simple static feedforward, or by a controller proposed by means of an internal model control (IMC). Another possible approach is to apply for the DIPDT model directly a stabilizing PID controller. The considered solutions are compared to the magnetic levitation system, controlled via the MATLAB/Simulink environment. It is shown that, all three controllers, with integral action, yield much slower dynamics than the stabilizing PD control, which gives one motivation to look for alternative ways of steady-state error compensation, guaranteeing faster setpoint step responses.

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