Two-Level Sensorimotor Learning for Leader-Follower Consensus Control

The present work addresses the problem of leader-follower consensus based on a bioinspired sensorymotor approach. The herein presented learning scheme entails two levels: (i) a supervised offline babbling training, where babbling generates a preliminary inference of the unknown environment, and (ii) during the consensus the execution (online) stage the cortical map is re-training throughout agents, which simultaneously to the consensus lapse the learning model is refined. The purpose of the proposed strategy is to link the human sensorimotor postural model with the consensus problem to endow of natural plasticity to the MAS. In order to fullfil the leader-follower control objective a controller based Lyapunov stability theory is synthesized. A set of numerical simulations are conducted to evaluate the MAS performance while following the cortical-mapped leader trajectory.