Cancer Growth Treatment by Adaptive Robust Immune Pole Placement Controller with Different Structures

Mohammed ayad Hussein, Ekhlas Hameed Karam


Despite medical and technological advancements that can detect and cure many cancer forms, cancer incidence and mortality rates are rising worldwide. A tumor-killing virus that infects and analyzes cancer cells while leaving most normal cells intact is known as an oncolytic virus. The mathematical model of interact between tumor cells and oncolytic viruses used to provide closely look to these technics used in cancer treatment.  In this article, an Adaptive Robust Immune Pole Placement (ARIPP) controller based on an Improved Crow Search Algorithm (ICSA) has been suggested to deliver oncolytic viruses. The control method was evaluated on a computer using MATLAB simulation. Furthermore, the dynamic uncertainty also tested, results show tumor cells reduced to a specific therapeutic zone. The suggested controller ARIPP I structure shows more excellent performance than structure II and III structure by 3.2707%, 3.5452%, respectively.

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Cancer Research UK, “Worldwide cancer statistics | Cancer Research UK,” Cancer Research UK. pp. 1–5, 2016. Accessed: Apr. 26, 2021. [Online]. Available:

NIH, “Cancer Statistics - National Cancer Institute,” NIH, 2016. (accessed Apr. 26, 2021).

A. L. JENNER, “Applications of mathematical modelling in oncolytic virotherapy and immunotherapy,” Bulletin of the Australian Mathematical Society, vol. 101, no. 3, pp. 522–524, 2020.

R. Padmanabhan, N. Meskin, and A.-E. al Moustafa, Mathematical Models of Cancer and Different Therapies: Unified Framework. Springer Nature, 2021.

A. K. Arum, D. Handayani, and R. Saragih, “Robust control design for virotherapy model using successive method,” in Journal of Physics: Conference Series, 2019, vol. 1245, no. 1, p. 12054.

J. J. Crivelli, J. Földes, P. S. Kim, and J. R. Wares, “A mathematical model for cell cycle-specific cancer virotherapy,” Journal of biological dynamics, vol. 6, no. sup1, pp. 104–120, 2012.

A. Kaur, R. Kaur, and S. Sondhi, “CSA based PID controller design technique for optimizing various integral errors,” in 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence), 2020, pp. 55–62.

A. Askarzadeh, “A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm,” Computers & Structures, vol. 169, pp. 1–12, 2016.

K. Takahashi and T. Yamada, “Application of an immune feedback mechanism to control systems,” JSME International Journal Series C Mechanical Systems, Machine Elements and Manufacturing, vol. 41, no. 2, pp. 184–191, 1998.

Y. Ding, L. Chen, and K. Hao, Bio-Inspired Collaborative Intelligent Control and Optimization. Springer, 2018.

J. Sun et al., “Optimal control model of tumor treatment with oncolytic virus and MEK inhibitor,” BioMed research international, vol. 2016, no. 3–4, pp. 3763–3775, 2016.

J. Malinzi, R. Ouifki, A. Eladdadi, D. F. M. Torres, and K. A. White, “Enhancement of chemotherapy using oncolytic virotherapy: mathematical and optimal control analysis,” arXiv preprint arXiv:1807.04329, 2018.

A. K. Arum, R. Saragih, and D. Handayani, “Bilinear robust control design for virotherapy model,” in 2019 19th International Conference on Control, Automation and Systems (ICCAS), 2019, pp. 82–86.

A. J. N. Anelone, M. F. Villa-Tamayo, and P. S. Rivadeneira, “Oncolytic virus therapy benefits from control theory,” Royal Society open science, vol. 7, no. 7, p. 200473, 2020.

M. F. Villa-Tamayo, A. J. N. Anelone, and P. S. Rivadeneira, “Tumor Reduction Using Oncolytic Viruses Under an Impulsive Nonlinear Estimation and Predictive Control Scheme,” IEEE Control Systems Letters, vol. 5, no. 5, pp. 1705–1710, 2020.

P.-H. Kim et al., “Active targeting and safety profile of PEG-modified adenovirus conjugated with herceptin,” Biomaterials, vol. 32, no. 9, pp. 2314–2326, 2011.

A. L. Jenner, C.-O. Yun, P. S. Kim, and A. C. F. Coster, “Mathematical modelling of the interaction between cancer cells and an oncolytic virus: insights into the effects of treatment protocols,” Bulletin of mathematical biology, vol. 80, no. 6, pp. 1615–1629, 2018.

A. L. Jenner, P. S. Kim, and F. Frascoli, “Oncolytic virotherapy for tumours following a Gompertz growth law,” Journal of theoretical biology, vol. 480, pp. 129–140, 2019.

X. Liu, X. Chen, X. Zheng, S. Li, and Z. Wang, “Development of a GA-fuzzy-immune PID controller with incomplete derivation for robot dexterous hand,” The Scientific World Journal, vol. 2014, 2014.

B. Rochdi, “Design and application of fuzzy immune PID control based on genetic optimization,” in International Workshop on Advanced Control IWAC, 2014, pp. 10–14.

Y. Meraihi, A. B. Gabis, A. Ramdane-Cherif, and D. Acheli, “A comprehensive survey of Crow Search Algorithm and its applications,” Artificial Intelligence Review, pp. 1–48, 2020.

Y. Shi and R. Eberhart, “A modified particle swarm optimizer,” in 1998 IEEE international conference on evolutionary computation proceedings. IEEE world congress on computational intelligence (Cat. No. 98TH8360), 1998, pp. 69–73.

J. Sun, W. Fang, V. Palade, X. Wu, and W. Xu, “Quantum-behaved particle swarm optimization with Gaussian distributed local attractor point,” Applied Mathematics and Computation, vol. 218, no. 7, pp. 3763–3775, 2011


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