| Title | Model Predictive Control for Tumor Growth: Detection of Deviations and Therapeutic Implications |
|---|---|
| Authors | Hernández-Rivera A. , VELARDE RUEDA, PABLO ANIBAL, Zafra-Cabeza A. , Maestre J.M. |
| External publication | No |
| Means | IFAC PAPERSONLINE |
| Scope | Conference Paper |
| Nature | Científica |
| Web | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85202883254&doi=10.1016%2fj.ifacol.2024.07.276&partnerID=40&md5=052de1cb565097bd5c5121b71db92fea |
| Publication date | 01/01/2024 |
| ISI | 001296047100093 |
| Scopus Id | 2-s2.0-85202883254 |
| DOI | 10.1016/j.ifacol.2024.07.276 |
| Abstract | Recent proposals for tumor reduction employ mathematical modeling and control methodologies to design improved drug administration schemes. This research aims to enhance chemotherapy treatment in mice by employing optimal control theories and fault detection algorithms. Firstly, a model predictive control (MPC) approach combined with sequential quadratic programming (SQP) is proposed to handle the non-linearities of the dynamics of chemotherapy, and second, a fault-tolerant scheme is introduced in the control loop for identifying abnormalities considered to be faults during the treatment and launching reconfiguration actions. Integrating these methodologies aids in the early detection of discrepancies between actual and expected outcomes, facilitating prompt and effective corrective actions. Therefore, this could improve the effectiveness of the chemotherapeutic cycle, reduce side effects, and potentially increase the success rate of treatments. Simulation results demonstrate that this combined approach provides a significantly more effective and secure therapeutic strategy. © 2024 The Authors. This is an open access article under the CC BY-NC-ND license. |
| Keywords | Controlled drug delivery; Linear control systems; Optimal control systems; Predictive control systems; Quadratic programming; % reductions; Chemotherapy treatment; Control methodology; Drug administration; Model-predictive control; Modeling methodology; Modelling and controls; Non linear system; Optimal control theory; Tumor growth; Chemotherapy |
| Universidad Loyola members |