Optimal and Model Free Control of Tumor Immune Interaction Dynamic to Schedule Cancer Treatments

Cancer is an intricate disease that can attack different parts of the human body. In the most common types of cancer, abnormal cells divide uncontrollably and impair body tissue. Cross disciplinary research has long aided expansion of our knowledge and ability to approach problems with a different perspective. Engineers and clinicians can collaborate to solve mysteries surrounding cancer cells function and responses. Engineers have contributed to cancer treatment, by studying new ways to diagnose and treat cancer. According to a study by John Hopkins university engineered Nano-particles can induce immune reaction and kill cancer cells. In addition, new ways of delivering cancer therapy to actuate the immune system to kill cancerous cells were found through engineering research.

The goal behind modelling biological systems is to drive the states to a desirable outcome using control elements in the dynamic system. In this thesis, we explore the effects of an Intelligent Proportional Integral Derivative (iPID) controllers; using optimal and model free control to improve the state of a cancer patient using recommended safe dosages. A non-linear mathematical model of Ordinary Differential Equations (ODE) is used to simulate a virtual cancer patient using realistic valued parameters. It is important to bear in mind that the human body is complex and variable. In particular, the immune response can vary from one patient to another. The parameters used to model the cancer have been deduced by clinicians and engineers to represent the tumor immune interaction using mathematical equations.

The dissertation will begin by exploring the cancer therapy's mathematical model, the controllability and observability to assure that the model is controllable, and explore different control methods and compare the results.