Nonlinear model predictive control using robust fixed point transformation-based phenomena for controlling tumor growth

Bence Czakó, L. Kovács

Research output: Contribution to journalArticle

Abstract

In this paper a novel control strategy is introduced in order to create optimal dosage profiles for individualized cancer treatment. This approach uses Nonlinear Model Predictive Control to construct optimal dosage protocols in conjunction with Robust Fixed Point Transformations which hinders the negative effect of inherent model uncertainties and measurement disturbances. The results are validated by extensive simulation on the proposed control algorithm from which conclusions were drawn.

Original languageEnglish
Article number49
JournalMachines
Volume6
Issue number4
DOIs
Publication statusPublished - Jan 1 2018

Fingerprint

Nonlinear Model Predictive Control
Oncology
Tumor Growth
Model predictive control
Tumors
Fixed point
Model Uncertainty
Control Algorithm
Control Strategy
Cancer
Disturbance
Simulation
Uncertainty
Profile

Keywords

  • Model predictive control
  • Nonlinear systems
  • Physiological control
  • Robust fixed point transformation

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science (miscellaneous)
  • Mechanical Engineering
  • Control and Optimization
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

Cite this

Nonlinear model predictive control using robust fixed point transformation-based phenomena for controlling tumor growth. / Czakó, Bence; Kovács, L.

In: Machines, Vol. 6, No. 4, 49, 01.01.2018.

Research output: Contribution to journalArticle

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