Robust control design for automatic regulation of blood pressure

Malagutti, Nicolò; Dehghani, Arvin; Kennedy, Rodney A.

doi:10.1049/iet-cta.2012.0254

Cited by 37 publications

(21 citation statements)

References 26 publications

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“…Therefore a multiple model structure is applied, as illustrated in Figure 2. The multiple model extended Kalman filter (MMEKF) consists of several EKFs in a parallel connection, each one assuming different injection delay values (Hanlon & Maybeck, 2000;Malagutti et al, 2012). Each individual EKF computes an a priori state estimation X i (k) and the corresponding covariance P i (k) as (Hanlon & Maybeck, 2000):…”

Section: On-line Estimation Of Model Parametersmentioning

confidence: 99%

“…where ω U can be used for representing maximal injection at low frequencies, obtained from the toxicity threshold of the given drug (Hahn et al, 2002;Malagutti et al, 2012). Other parameters of W u (s) are used for penalizing the high frequency behavior of the drug injection.…”

Section: Augmented Systemmentioning

confidence: 99%

“…Model predictive control (MPC) ideas have been reported in (Rao, Aufderheide & Bequette, 2003;Yu, Roy, Kaufman & Bequette, 1992), where the injection rates are obtained through a rolling horizon optimization. Recently, a robust multiple-model approach appeared in (Malagutti, Dehghani and Kennedy, 2012;Malagutti, 2014). These solutions use a multiple-model linear Kalman filtering framework for estimating a dynamical model.…”

Section: Introductionmentioning

confidence: 99%

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Robust linear parameter-varying control of blood pressure using vasoactive drugs

Luspay

Grigoriadis

2015

International Journal of Control

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Resuscitation of emergency care patients requires fast restoration of blood pressure to a target value to achieve hemodynamic stability and vital organ perfusion. A robust control design methodology is presented in this paper for regulating blood pressure of hypotensive patients by means of closed-loop administration of vasoactive drugs. To this end, a dynamic first-order delay model is utilized to describe the vasoactive drug response with varying parameters that represent intra-patient and inter-patient variability. The proposed framework consists of two components: firstly, an on-line model parameter estimation is carried out using a multiple-model extended Kalman-filter. Secondly, the estimated model parameters are used for continuously scheduling a robust linear parameter varying (LPV) controller. The closed-loop behavior is characterized by parameter-varying dynamic weights designed to regulate the mean arterial pressure to a target value. Experimental data of blood pressure response of anesthetized pigs to phenylephrine injection are used for validating the LPV blood pressure models. Simulation studies are provided to validate the on-line model estimation and the LPV blood pressure control using phenylephrine drug injection models representing patients showing sensitive, nominal and insensitive response to the drug.

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Section: On-line Estimation Of Model Parametersmentioning

confidence: 99%

Section: Augmented Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robust linear parameter-varying control of blood pressure using vasoactive drugs

Luspay

Grigoriadis

2015

International Journal of Control

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“…Controllers used in hemodynamic stability systems adjust the infusion rate and/or time of delivery of fluids (e.g., colloids, crystalloids, or blood) and/or vasoactive drugs (e.g., SNP, phenylephrine). A variety of controller designs have been tested with CPMs including single input-single output adaptive and model predictive controllers (Malagutti et al, 2013; Silva et al, 2017), rule-based learning systems (Rinehart et al, 2011), PID controllers (Wassar et al, 2014; Bighamian et al, 2016), and multi-input–multi-output systems that control multiple drugs simultaneously (Held and Roy, 1995; Huang and Roy, 1998; Rao et al, 2000). System designs may include supervisory components that add a layer of safety by monitoring for known system limitations, such as noise/signal artifacts in the sensed physiological variables that could adversely impact the controller performance (artin et al, 1992).…”

Section: Closed-loop Systems For Hemodynamic Stabilitymentioning

confidence: 99%

Credibility Evidence for Computational Patient Models Used in the Development of Physiological Closed-Loop Controlled Devices for Critical Care Medicine

et al. 2019

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Physiological closed-loop controlled medical devices automatically adjust therapy delivered to a patient to adjust a measured physiological variable. In critical care scenarios, these types of devices could automate, for example, fluid resuscitation, drug delivery, mechanical ventilation, and/or anesthesia and sedation. Evidence from simulations using computational models of physiological systems can play a crucial role in the development of physiological closed-loop controlled devices; but the utility of this evidence will depend on the credibility of the computational model used. Computational models of physiological systems can be complex with numerous nonlinearities, time-varying properties, and unknown parameters, which leads to challenges in model assessment. Given the wide range of potential uses of computational patient models in the design and evaluation of physiological closed-loop controlled systems, and the varying risks associated with the diverse uses, the specific model as well as the necessary evidence to make a model credible for a use case may vary. In this review, we examine the various uses of computational patient models in the design and evaluation of critical care physiological closed-loop controlled systems (e.g., hemodynamic stability, mechanical ventilation, anesthetic delivery) as well as the types of evidence (e.g., verification, validation, and uncertainty quantification activities) presented to support the model for that use. We then examine and discuss how a credibility assessment framework (American Society of Mechanical Engineers Verification and Validation Subcommittee, V&V 40 Verification and Validation in Computational Modeling of Medical Devices) for medical devices can be applied to computational patient models used to test physiological closed-loop controlled systems.

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“…The effect-site compartment modeling with non-linearity is usually expressed in terms of Sigmoid Hill equation [15][16][17][18][19] as shown in equations (7) …”

Section: Model Descriptionmentioning

confidence: 99%

Model-Based Approach on the Effect of Isoflurane and Control of Critical Hemodynamic Variables

Sowparnika¹,

Thirumarimurugan²,

Sivakumar³

et al. 2017

Int. Res. J. Pharm.

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Monitoring and control of physiological variables during surgeries is a difficult task since the manual infusion of drugs leads to complications in patients. Anaesthesia is mainly used to maintain most the critical physiological parameters. Automatic control of these para meters will help the anaesthetist to focus on other issues that has to be taken care of. This paper presents about the closed loop control of anaesthesia drug that is used to maintain the safety limits of physiological variables such as hypnosis in terms of Bi-Spectral Index (BIS), analgesia in terms of Mean Arterial Pressure (MAP) and Heart Rate (HR). Here isoflurane is anaesthesia that is used as the medicine to administer the parameters. Pharma cokinetic (PK) and Pharmacodynamic (PD) modeling is carried out for isoflurane drug and control action is implemented based on the open loop response of the PK-PD model. The performance of the model is compared for Proportional-Integral (PI) controller and Neural Network Internal Model Control (NN-IMC) and the simulation results are obtained using MATLAB.

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Robust control design for automatic regulation of blood pressure

Cited by 37 publications

References 26 publications

Robust linear parameter-varying control of blood pressure using vasoactive drugs

Robust linear parameter-varying control of blood pressure using vasoactive drugs

Credibility Evidence for Computational Patient Models Used in the Development of Physiological Closed-Loop Controlled Devices for Critical Care Medicine

Model-Based Approach on the Effect of Isoflurane and Control of Critical Hemodynamic Variables

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