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
收藏
页码：215 / 220
页数：6
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