The ability to adapt to a continuously changing environment and robustness to disturbances and uncertainties are always crucial features for a controller. In the last decades, an increasing interest in modeling the Limbic system in the human brain led to the development of the Brain Emotional Learning-Based Intelligent Controller (BELBIC) in order to cope with unknown dynamics. The novelty brought by this system, compared to a Neural Adaptive, is the ability to have a memory of past experiences, learn from them and adapt itself, providing a faster response when the same scenario is proposed again. In this thesis, the BELBIC controller is used to control a heterogeneous Multi-Agent System composed of a fleet of drones following a mobile robot leader, both for consensus and formation purposes.