Robust delay-dependent LPV output-feedback blood pressure control with real-time Bayesian estimation

TL;DR

This paper presents a robust LPV output-feedback control method with real-time Bayesian delay estimation for blood pressure regulation, improving response accuracy and robustness in critical patient care scenarios.

Contribution

It introduces a Bayesian-based multiple-model Kalman filtering approach for real-time delay and parameter estimation, combined with a delay-dependent LPV control scheme for blood pressure regulation.

Findings

Effective MAP regulation demonstrated in animal experiments.

Robust control maintains performance despite uncertainties and disturbances.

Simulation results confirm improved response and robustness.

Abstract

Mean arterial blood pressure (MAP) dynamics estimation and its automated regulation could benefit the clinical and emergency resuscitation of critical patients. In order to address the variability and complexity of the MAP response of a patient to vasoactive drug infusion, a parameter-varying model with a varying time delay is considered to describe the MAP dynamics in response to drugs. The estimation of the varying parameters and the delay is performed via a Bayesian-based multiple-model square root cubature Kalman filtering approach. The estimation results validate the effectiveness of the proposed random-walk dynamics identification method using collected animal experiment data. Following the estimation algorithm, an automated drug delivery scheme to regulate the MAP response of the patient is carried out via time-delay linear parameter-varying (LPV) control techniques. In this regard, an LPV gain-scheduled output-feedback controller is designed to meet the MAP response requirements of tracking a desired reference MAP target and guarantee robustness against norm-bounded uncertainties and disturbances. In this context, parameter-dependent Lyapunov-Krasovskii functionals are used to derive sufficient conditions for the robust stabilization of a general LPV system with an arbitrarily varying time delay and the results are provided in a convex linear matrix inequality (LMI) constraint framework. Finally, to evaluate the performance of the proposed MAP regulation approach, closed-loop simulations are conducted and the results confirm the effectiveness of the proposed control method against various simulated clinical scenarios.