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Machines 2018, 6(4), 49; https://doi.org/10.3390/machines6040049

Nonlinear Model Predictive Control Using Robust Fixed Point Transformation-Based Phenomena for Controlling Tumor Growth

†,* and †,*
Physiological Controls Research Center, EKIK, Obuda University, 1034 Budapest, Hungary
These authors contributed equally to this work.
*
Authors to whom correspondence should be addressed.
Received: 31 August 2018 / Revised: 9 October 2018 / Accepted: 17 October 2018 / Published: 25 October 2018
(This article belongs to the Special Issue Advanced Control Systems and Optimization Techniques)
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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. View Full-Text
Keywords: physiological control; nonlinear systems; model predictive control; robust fixed point transformation physiological control; nonlinear systems; model predictive control; robust fixed point transformation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Czakó, B.; Kovács, L. Nonlinear Model Predictive Control Using Robust Fixed Point Transformation-Based Phenomena for Controlling Tumor Growth. Machines 2018, 6, 49.

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