Chemotherapy Optimization using Moving Horizon Estimation based Nonlinear Model Predictive Control

被引：2

作者：

Czako, Bence ^{[1
]}

Siket, Mate ^{[1
]}

Drexler, Daniel Andras ^{[1
]}

Kovacs, Levente ^{[1
]}

机构：

[1] Obuda Univ, Physiol Controls Res Ctr, Res & Innovat Ctr, Budapest, Hungary

来源：

IFAC PAPERSONLINE | 2021年 / 54卷 / 15期

基金：

欧洲研究理事会; 欧盟地平线“2020”;

关键词：

Physiological Model; Parameter and state estimation; Nonlinear predictive control; Robustness analysis;

D O I：

10.1016/j.ifacol.2021.10.258

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A Moving Horizon Estimator (MHE) based Nonlinear Model Predictive Controller (NMPC) was designed for an impulsive minimal tumor growth model. The estimator computes the time-varying model parameters using mean square error with parameter deviation penalization and provides state estimations for the controller. The controller computes optimal doses for non-equidistant, fixed time instants while constraining the administered drug dose. Tuning of the MHE was based on experimental time series measurements, while for the NMPC a virtual population was generated. The robustness of the combined approach was tested in silico on a virtual population, where the simulation was tailored to a real experimental scenario. Copyright (C) 2021 The Authors.

引用

页码：215 / 220

页数：6

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