In the last decades beside conventional cancer treatment methods, molecular targeted therapies show prosperous results. These therapies have limited side-effects, and in comparison to chemotherapy, tumorous cells show lower tendency of becoming resistant to the applied antiangiogenic drugs. In clinical research, antiangiogenic therapy is one of the most promising cancer treatment methods. A new aspect of medical research is that mathematical models are posed to optimize the applied treatments based on biologically validated experiments. In this thesis, a simplified model of the reference dynamical model for tumor growth under angiogenic inhibition is used. The aim of the thesis project was to elaborate and implement control techniques which secure fast tumor regression using low inhibitor inlets. Four basically different control methodologies are investigated: robust control methods (H∞ and μ synthesis), optimal regulations (LQ, bang-bang), control techniques based on exact linearization (flat and flatness based switch control), and artificial intelligence methods (fuzzy and adaptive fuzzy control). The assessment and comparison of the presented methods include how the designed controllers perform in the case of parameter perturbations of high degree.