  This paper presents the application of a Distributed Model Reference Adaptive
Control (DMRAC) strategy for robust multi-agent synchronization of a network of
drones. The proposed approach enables the development of controllers capable of
accommodating differences in real-life model parameters between agents, thereby
enhancing overall network performance. We compare the performance of the
adaptive control laws with classical PID controllers for the reference tracking
task. Each follower drone has a model reference adaptive controller that
continuously updates its parameters based on real-time feedback and reference
model information. This adaptability ensures an adequate performance that,
compared to conventional non-adaptive techniques, can reduce the amount of
energy required and consequently increase the operating duration of the drones.
The experimental results, particularly in vertical velocity control, underscore
the effectiveness of the proposed approach in achieving synchronized behavior.
