Design immune robust integral signum of the error controller for cancer tumor growth treatment based on improved crow search algorithm

Abstract

Cancer is one of the most common diseases around the world, which is difficult to treat due to the versatility and adaptability of cancer cells for facing normal cell death and the invisibility of the immune system. Many types of treatments have been proposed in the literature to deal with this disease, one of these promising methods is the Oncolytic Virotherapy (OV) technique. Controllers have an essential role in this process as they help in carefully managing injection periods, determining appropriate doses, and facing uncertainty in biological parameters. In this paper, an adaptive controller is designed Immune Robust Integral Signum of the Error (IRISE) that give and schedule optimal medication doses to control tumor growth. In addition, an Improved Crow Search Algorithm (ICSA) was used to obtain the optimal parameters for this controller. The controller was validated using a mathematical model of the Interaction Between Tumor Cells and Oncolytic Virus (ITCV) on several cases in different conditions of uncertainty and disturbances. Using a continuous controller to maintain the viral concentration helps to reduce the very high doses of medication needed at the start of therapy, as the viral load can be reduced by half, which in turn reduces concerns about toxicity and costs. The comparative results showed the IRISE based ICSA outperforms the CSA by 0.0 % and 0.02 % at subjects S1 and S3 respectively. In addition, the performance of IRISE controller accommodated most the ITCV model uncertainty in subject S3. In contrast, failure in subject S1 to accommodate stromal cells presence and parameters uncertainty.