R.R. Rao

Experimental studies on multiple-model predictive control for automated regulation of hemodynamic variables

A model-based control methodology was developed for automated regulation of mean arterial pressure and cardiac output in critical care subjects using inotropic and vasoactive drugs. The control algorithm used a multiple-model adaptive approach in a model predictive control framework to account for variability and explicitly handle drug rate constraints. The controller was experimentally evaluated on canines that were pharmacologically altered to exhibit symptoms of hypertension and depressed cardiac output. The controller performed better as compared to experiments on manual regulation of the hemodynamic variables. After the model bank was determined, mean arterial pressure was held within /spl plusmn/5 mm Hg 88.9% of the time with a standard deviation of 3.9 mmHg. The cardiac output was held within /spl plusmn/1 l/min 96.1% of the time with a standard deviation of 0.5 l/min. The manual runs maintain mean arterial pressure only 82.3% of the time with a standard deviation of 5 mmHg, and cardiac output 92.2% of the time with a standard deviation of 0.6 l/min.

Automated regulation of hemodynamic variables

Experimental studies of two control methodologies for regulating multiple variables in critical care patients are described. The control strategies for the regulation of mean arterial pressure and cardiac output use vasoactive and inotropic drugs. Corresponding experimental results from the evaluation of the controllers with canines are presented.

Multiple model predictive control of hemodynamic variables: an experimental study

A multiple model predictive controller is designed to regulate mean arterial pressure and cardiac output in critical care subjects using inotropic and vasoactive drugs. The algorithm uses a multiple model adaptive approach in a model predictive control framework to account for inter- and intra-patient variability and explicitly handle drug rate constraints. The controller is experimentally evaluated on canines that are pharmacologically altered to exhibit symptoms of hypertension and depressed cardiac output.

Simultaneous regulation of hemodynamic and anesthetic states: a simulation study.

AbstractA model predictive control strategy to simultaneously regulate hemodynamic and anesthetic variables in critical care patients is presented. A nonlinear canine circulatory model, which has been used to study the effect of inotropic and vasoactive drugs on hemodynamic variables, has been extended to include propofol pharmacokinetics and pharmacodynamics. Propofol blood concentration is used as a measure for depth of anesthesia. The simulation model is used to design and test the control strategy. The optimization-based model predictive control strategy assures that constraints imposed on the drug infusion rates are met. The physician always remains “in the loop” and serves as the “primary controller” by making propofol blood concentration setpoint changes based on observations about anesthetic depth. Results are shown for three simulated cases: (i) congestive heart failure, (ii) postcoronary artery bypass, and (iii) acute changes in hemodynamic variables.

Control of a nonsquare drug infusion system: A simulation study.

A model predictive control strategy was developed and tested on a nonlinear canine circulatory model for the regulation of hemodynamic variables under critical care conditions. Different patient conditions such as congestive heart failure, post‐operative hypertension, and sepsis shock were studied in closed‐loop simulations. The model predictive controller, which uses a different linear model depending on the patient condition, allowed constraints to be explicitly enforced. The controller was initially tuned on the basis of a linear plant model, then tested on the nonlinear physiological model; the simulations demonstrated the ability to handle constraints, such as drug dosage specifications, commonly desired by critical care physicians.

Modeling and Control of a Nonsquare Drug Infusion System

Abstract A model predictive control strategy is developed and tested on an nonlinear canine circulatory model for the regulation of hemodynamic variables under critical care conditions. Several cases are studied, including congestive heart failure, post-operative hypertension and a patient that moves from hypertensive to hypotensive conditions. The “nonsquare” (more process inputs than outputs) control system allows the independent management of the hemodynamic and venous circulation. The model predictive controller, which uses a different linear model depending on the patient condition, allows constraints to be explicitly enforced. The controller is initially tuned based on linear plant responses, then tested on the nonlinear plant model; the simulations verify the robustness of the control strategy

Multiple model predictive control of canine hemodynamic variables

A multiple model predictive controller is designed to regulate mean arterial pressure, and cardiac output using inotropic and vasoactive drugs. The algorithm accounts for inter- and intra-patient variability and explicitly handles drug rate constraints. It is experimentally evaluated on canines that are pharmacologically altered to exhibit symptoms of hypertension and depressed cardiac output.

Automated hemodynamic regulation with model predictive control

A multiple model adaptive predictive controller is designed to regulate mean arterial pressure and cardiac output in critical care subjects using inotropic and vasoactive drugs. The algorithm uses a multiple model adaptive approach in a model predictive control framework to account for inter- and intra-patient variability and explicitly handle drug rate constraints. The controller is evaluated on canines that were pharmacologically altered to exhibit symptoms of hypertension and depressed cardiac output.