High Performance Computing (HPC) system is hosted at Grenoble Alpes University. In recent years, researches have been done in High Performance Computing in the field of control theory for cluster autonomic management; objective of those previous works was to optimize the cluster’s usage. The system is not fully used by its main users, and a component named ”CiGri” has been added to inject extra tasks on the system to fully use it. The extra tasks share both the computation resources and the file system for storing intermediate data. The injection of extra tasks is managed with a feedback loop. As the environment is very heterogeneous and unstable, we proposed to use the Model Free Control (MFC) framework to dynamically manage the utilization of the cluster. The goal is to increase the utilization of the system while avoiding the overload of the file system. We used MFC with the following features: 1) a ’virtual model’ simple representing the unknown system dynamics, 2) elimination of the need of complex system parameter esti- mation, and 3) assurance of reduced computational costs. The conducted work explores and adopts MFC using the intelligent P control law, which assures both robustness and quality reference tracking with respect to time varying system dynamics. After demon- strating the effectiveness of the proposed scheme on a simple simulated first-order model with intelligent proportional controller, we managed to track very well the references. The MFC gain is too big which leads to the oscillation and overshoots at the beginning. The proposed MFC gain has the selective chosen regarding the reference values which will be send a constant gain if there is no change in the reference, otherwise it is multiplied by the gain and sent to the controller to have a good tracking of the reference.