  The flock-guidance problem enjoys a challenging structure where multiple
optimization objectives are solved simultaneously. This usually necessitates
different control approaches to tackle various objectives, such as guidance,
collision avoidance, and cohesion. The guidance schemes, in particular, have
long suffered from complex tracking-error dynamics. Furthermore, techniques
that are based on linear feedback strategies obtained at equilibrium conditions
either may not hold or degrade when applied to uncertain dynamic environments.
Pre-tuned fuzzy inference architectures lack robustness under such unmodeled
conditions. This work introduces an adaptive distributed technique for the
autonomous control of flock systems. Its relatively flexible structure is based
on online fuzzy reinforcement learning schemes which simultaneously target a
number of objectives; namely, following a leader, avoiding collision, and
reaching a flock velocity consensus. In addition to its resilience in the face
of dynamic disturbances, the algorithm does not require more than the agent
position as a feedback signal. The effectiveness of the proposed method is
validated with two simulation scenarios and benchmarked against a similar
technique from the literature.
