Document Type : Research Article
Authors
1 Department of electrical engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
2 Department of Mathematics, Payame Noor University, Iran.
10.30473/coam.2025.74268.1300
Abstract
This study analyzes the growth dynamics of melanoma tumor cells and develops a model predictive controller (MPC) using four well-known optimizers to suppress tumor growth, proposing an MPC framework that integrates multiple metaheuristic algorithms for regulating tumor size. All modelling, control design, and simulations are performed in MATLAB, and results indicate that a PSO-based MPC offers satisfactory response and rapid convergence, achieving effective tracking and disturbance rejection. The study assumes precise drug dosing is feasible and demonstrates substantial tumor-size reduction through the integration of MPC with metaheuristic optimization. Simulation findings reveal that the PSO-based MPC achieves notable improvement in tumor reduction and overall control performance, outperforming other metaheuristic approaches, as evidenced by comparative error metrics: ITAE ≈ 1.9377 × 10^3, IAE ≈ 244.45, MSE ≈ 4.6863 × 10^3.
Highlights
- Proposes a novel Model Predictive Control (MPC) framework that concurrently leverages multiple metaheuristic optimizers (with a standout PSO-based MPC) to regulate tumor size, enabling adaptive and robust therapy planning.
- Combines a dynamic mathematical model of melanoma growth with recursive least squares parameter estimation and an MPC that is tailored to individual tumor dynamics and drug responses, supporting personalized treatment strategies.
- Introduces an inter-sample heterogeneity-aware rest period (one day per cycle) determined through a predictive control and optimization loop, highlighting the importance of dosing cadence in optimizing outcomes.
- Demonstrates that PSO-enhanced MPC outperforms other metaheuristic approaches in tumor size reduction, disturbance rejection, and control quality, as evidenced by metrics such as ITAE, IAE, and MSE.
- Shows that the integrated MPC framework can maintain tumor control under intrinsic drug resistance and model uncertainties, with substantial tumor reduction while minimizing harm to healthy tissue.
- All modelling, control design, and simulations are conducted in MATLAB, with explicit reporting of performance metrics and a clear pathway for replication and validation across labs.
- Provides a structured process for deriving predicted treatment plans, including dosage schedules, that can inform experimental designs and, in the long run, clinical translation.
- Establishes concrete comparative benchmarks (e.g., ITAE ≈ 1.94×10^3, IAE ≈ 244.5, MSE ≈ 4.69×10^3) to guide subsequent studies aiming to optimize combinatorial control-algorithm strategies in oncologic pharmacology.
Keywords
- Model predictive control
- Melanoma cancer
- Tumor growth control
- Biomedical control systems
- Metaheuristic optimization
Main Subjects
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