In our previous work, we have introduced an adaptive hierarchical scheduling framework as a solution for composing dynamic real-time systems, i.e., systems where the CPU demand of their tasks are subjected to unknown and potentially drastic changes during run-time. The framework uses the PI controller which periodically adapts the system to the current load situation. The conventional PI controller despite simplicity and low CPU overhead, provides acceptable performance. However, increasing the pressure on the controller e.g, with an application consisting of multiple tasks with drastically oscillating execution times, degrades the performance of the PI controller. Therefore, in this paper we modify the structure of our adaptive framework by replacing the PI controller with a fuzzy controller to achieve better performance. Furthermore, we conduct a simulation based case study in which we compose dynamic tasks such as video decoder tasks with a set of static tasks into a single system, and we show that the new fuzzy controller outperforms our previous PI controller.