Design and in Silico Evaluation of a Closed-Loop Hemorrhage Resuscitation Algorithm with Blood Pressure as Controlled Variable

2021 ◽  
Author(s):  
Mohammad Alsalti ◽  
Ali Tivay ◽  
Xin Jin ◽  
George Kramer ◽  
Jin-Oh Hahn
Keyword(s):  
Blood Pressure ◽  
Adaptive Control ◽  
In Silico ◽  
Closed Loop ◽  
Control Design ◽  
Control Algorithms ◽  
Model Based Control ◽  
Knowledge Based ◽  
Performance Error ◽  
Composite Adaptive Control

Abstract This paper concerns the design and rigorous in silico evaluation of a closed-loop hemorrhage resuscitation algorithm with blood pressure (BP) as controlled variable. A lumped-parameter control design model relating volume resuscitation input to blood volume (BV) and BP responses was developed and experimentally validated. Then, three alternative adaptive control algorithms were developed using the control design model: (i) model reference adaptive control with BP feedback, (ii) composite adaptive control with BP feedback, and (iii) composite adaptive control with BV and BP feedback. To the best of our knowledge, this is the first work to demonstrate model-based control design for hemorrhage resuscitation with readily available BP as feedback. The efficacy of these closed-loop control algorithms was comparatively evaluated as well as compared with an empiric expert knowledge-based algorithm based on 100 realistic virtual patients created using a well-established physiological model of cardiovascular hemodynamics. The in silico evaluation results suggested that the adaptive control algorithms outperformed the knowledge-based algorithm in terms of both accuracy and robustness in BP set point tracking: the average median performance error and median absolute performance error were significantly smaller by >99% and >91%, and as well, their inter-individual variability was significantly smaller by >88% and >94%. Pending in vivo evaluation, model-based control design may advance the medical autonomy in closed-loop hemorrhage resuscitation.

For model-based control design, closed-loop identification gives better performance

Automatica ◽  
1996 ◽  
Vol 32 (12) ◽  
pp. 1659-1673 ◽  
Author(s):  
Håkan Hjalmarsson ◽  
Michel Gevers ◽  
Franky de Bruyne
Keyword(s):  
Closed Loop ◽  
Control Design ◽  
Model Based Control ◽  
Model Based ◽  
Closed Loop Identification

Closed-loop Control of Mean Arterial Blood Pressure during Surgery with Alfentanil

2006 ◽  
Vol 105 (3) ◽  
pp. 462-470 ◽  
Author(s):  
Martin Luginbühl ◽  
Christian Bieniok ◽  
Daniel Leibundgut ◽  
Rolf Wymann ◽  
Andrea Gentilini ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Arterial Blood Pressure ◽  
Mean Arterial Blood Pressure ◽  
Closed Loop ◽  
Closed Loop Control ◽  
Loop Control ◽  
Set Point ◽  
Arterial Blood ◽  
Performance Error ◽  
The Mean

Background In contrast to hypnosis, there is no surrogate parameter for analgesia in anesthetized patients. Opioids are titrated to suppress blood pressure response to noxious stimulation. The authors evaluated a novel model predictive controller for closed-loop administration of alfentanil using mean arterial blood pressure and predicted plasma alfentanil concentration (Cp Alf) as input parameters. Methods The authors studied 13 healthy patients scheduled to undergo minor lumbar and cervical spine surgery. After induction with propofol, alfentanil, and mivacurium and tracheal intubation, isoflurane was titrated to maintain the Bispectral Index at 55 (+/- 5), and the alfentanil administration was switched from manual to closed-loop control. The controller adjusted the alfentanil infusion rate to maintain the mean arterial blood pressure near the set-point (70 mmHg) while minimizing the Cp Alf toward the set-point plasma alfentanil concentration (Cp Alfref) (100 ng/ml). Results Two patients were excluded because of loss of arterial pressure signal and protocol violation. The alfentanil infusion was closed-loop controlled for a mean (SD) of 98.9 (1.5)% of presurgery time and 95.5 (4.3)% of surgery time. The mean (SD) end-tidal isoflurane concentrations were 0.78 (0.1) and 0.86 (0.1) vol%, the Cp Alf values were 122 (35) and 181 (58) ng/ml, and the Bispectral Index values were 51 (9) and 52 (4) before surgery and during surgery, respectively. The mean (SD) absolute deviations of mean arterial blood pressure were 7.6 (2.6) and 10.0 (4.2) mmHg (P = 0.262), and the median performance error, median absolute performance error, and wobble were 4.2 (6.2) and 8.8 (9.4)% (P = 0.002), 7.9 (3.8) and 11.8 (6.3)% (P = 0.129), and 14.5 (8.4) and 5.7 (1.2)% (P = 0.002) before surgery and during surgery, respectively. A post hoc simulation showed that the Cp Alfref decreased the predicted Cp Alf compared with mean arterial blood pressure alone. Conclusion The authors' controller has a similar set-point precision as previous hypnotic controllers and provides adequate alfentanil dosing during surgery. It may help to standardize opioid dosing in research and may be a further step toward a multiple input-multiple output controller.

Semi-Adaptive Closed-Loop Control for Infusion of Medications With Transport Delay in Clinical Effects

2019 ◽  
Vol 14 (10) ◽  
Author(s):  
Xin Jin ◽  
Chang-Sei Kim ◽  
Steven T. Shipley ◽  
Guy A. Dumont ◽  
Jin-Oh Hahn
Keyword(s):  
Adaptive Control ◽  
Closed Loop ◽  
Padé Approximation ◽  
Control Design ◽  
Closed Loop Control ◽  
Clinical Effects ◽  
Pade Approximation ◽  
Loop Control ◽  
Transport Delay ◽  
Wide Range

Abstract This paper presents a semi-adaptive closed-loop control approach to autonomous infusion of medications exhibiting significant transport delay in clinical effects. The basic idea of the approach is to enable stable adaptive control of medication infusion by (1) incorporating transport delay explicitly into control design by way of a Padé approximation while (2) facilitating linear parameterization of control design model by desensitization of nonlinearly parameterized cooperativity constant associated with pharmacodynamics (PD). A novel dynamic dose–response model for control design is presented, in which the cooperativity constant exerts zero influence on the model output in the steady-state. Then, an adaptive pole placement control (APPC) technique was employed to fulfill adaptive control design in the presence of nonminimum phase dynamics associated with the Padé approximation of transport delay. The controller was evaluated in silico using a case study of regulating a cardiovascular variable with a sedative under a wide range of transport delay and pharmacological profiles. The results suggest that adaptation of transport delay and pharmacological characteristics may be beneficial in achieving consistent and robust regulation of medication-elicited clinical effects.

scholarly journals Comparative Analysis of Identification Methods for Mechanical Dynamics of Large-Scale Wind Turbine

Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3429 ◽  
Author(s):  
Chu ◽  
Yuan ◽  
Hu ◽  
Pan ◽  
Pan
Keyword(s):  
Mutual Information ◽  
Parameter Identification ◽  
Wind Turbines ◽  
Closed Loop ◽  
Control Design ◽  
Identification Accuracy ◽  
Mass Model ◽  
Identification Methods ◽  
Advanced Control ◽  
Mechanical Dynamics

With increasing size and flexibility of modern grid-connected wind turbines, advanced control algorithms are urgently needed, especially for multi-degree-of-freedom control of blade pitches and sizable rotor. However, complex dynamics of wind turbines are difficult to be modeled in a simplified state-space form for advanced control design considering stability. In this paper, grey-box parameter identification of critical mechanical models is systematically studied without excitation experiment, and applicabilities of different methods are compared from views of control design. Firstly, through mechanism analysis, the Hammerstein structure is adopted for mechanical-side modeling of wind turbines. Under closed-loop control across the whole wind speed range, structural identifiability of the drive-train model is analyzed in qualitation. Then, mutual information calculation among identified variables is used to quantitatively reveal the relationship between identification accuracy and variables’ relevance. Then, the methods such as subspace identification, recursive least square identification and optimal identification are compared for a two-mass model and tower model. At last, through the high-fidelity simulation demo of a 2 MW wind turbine in the GH Bladed software, multivariable datasets are produced for studying. The results show that the Hammerstein structure is effective for simplify the modeling process where closed-loop identification of a two-mass model without excitation experiment is feasible. Meanwhile, it is found that variables’ relevance has obvious influence on identification accuracy where mutual information is a good indicator. Higher mutual information often yields better accuracy. Additionally, three identification methods have diverse performance levels, showing their application potentials for different control design algorithms. In contrast, grey-box optimal parameter identification is the most promising for advanced control design considering stability, although its simplified representation of complex mechanical dynamics needs additional dynamic compensation which will be studied in future.

Composite adaptive control based on historical data for electro-hydraulic system driven by dual servo motor-pump with unknown disturbance

2021 ◽  
pp. 095965182199860
Author(s):  
Xiaofu Zhang ◽  
Guanglin Shi
Keyword(s):  
Adaptive Control ◽  
Hydraulic System ◽  
Historical Data ◽  
Tracking Performance ◽  
Load Force ◽  
Position Tracking ◽  
Servo Motor ◽  
Estimated Parameters ◽  
Unknown Disturbance ◽  
Composite Adaptive Control

This article presents a composite adaptive control method to improve the position-tracking performance of an electro-hydraulic system driven by dual constant displacement pump and dual servo motor named as a novel electro-hydraulic system with unknown disturbance. A composite adaptive controller based on backstepping method is designed to estimate the uncertainties of electro-hydraulic control system, including the damping coefficient and elastic modulus. In order to release the persistent excitation condition of conventional adaptive control, which is often infeasible in practice, a prediction error based on the online historical data is used to update the estimated parameters. Furthermore, a disturbance observer is used to estimate the disturbance including the unmeasurable load force, friction and other unmodeled disturbance. The experiment results are provided and compared with other methods to verify the effectiveness of the proposed method, and the results have indicated that the proposed method has a better position-tracking performance with the convergent estimated parameters.

Sign in / Sign up

Export Citation Format

Share Document