| Título | Optimal Drug Administration in Cancer Therapy using Stochastic Non-Linear Model Predictive Control |
|---|---|
| Autores | Hernández-Rivera A. , VELARDE RUEDA, PABLO ANIBAL, Zafra-Cabeza A. , Maestre J.M. |
| Publicación externa | No |
| Medio | European Control Conf., ECC |
| Alcance | Conference Paper |
| Naturaleza | Científica |
| Web | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200549668&doi=10.23919%2fECC64448.2024.10591253&partnerID=40&md5=bafd171c4ff32e54484062e2e454fedb |
| Fecha de publicacion | 01/01/2024 |
| Scopus Id | 2-s2.0-85200549668 |
| DOI | 10.23919/ECC64448.2024.10591253 |
| Abstract | There has been significant interest in using advanced control strategies for medical treatments in recent years. This study proposes a two-fold approach to enhance drug dosing in cancer treatment. Firstly, a stochastic model predictive control (SMPC) is designed to address the uncertainties inherent in patient responses. Secondly, this SMPC is formulated as a sequential quadratic programming (SQP) MPC to manage the system\'s non-linearities. Therefore, this study proposes a stochastic SQP-MPC drug delivery framework to enhance patient outcomes and reduce side effects. The effectiveness of the proposed strategy is assessed via simulations and compared with other strategies. © 2024 EUCA. |
| Palabras clave | Controlled drug delivery; Diseases; Model predictive control; Predictive control systems; Quadratic programming; Stochastic control systems; Stochastic models; Advanced control strategy; Cancer therapy; Drug administration; Medical treatment; Nonlinear model predictive control; Patient response; Sequential quadratic programming; Stochastic model predictive controls; Stochastics; Uncertainty; Stochastic systems |
| Miembros de la Universidad Loyola |