Two-Level Sensorimotor Learning for Leader-Follower Consensus Control

J. Franco-Robles (1) , J. Escareno (2) , O. Labbani-Igbida (3)

1 3iL Ingénieurs
2 XLIM Research Institute, UMR CNRS 7252, University of Limoges, 87000 Limoges, France
3 XLIM Research In- stitute, UMR CNRS 7252, University of Limoges, 87000 Limoges, France

The present work addresses the problem of leader-follower consensus based on a bioinspired sensory motor 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.

Mots clés: Leader-following Consensus; Leader-following Consensus Control; Numerical Simulations; Cortical Function; Multi-agent Systems; Sensory Motor; Online Learning; Unmanned Aerial Vehicles; Body Schema

Domaines: Système multi-agents [cs.MA]

J. Franco-Robles, J. Escareno, O. Labbani-Igbida. Two-Level Sensorimotor Learning for Leader-Follower Consensus Control. IEEE Conference on Decision and Control and European Control Conference (CDC-ECC), 2023, pp.1-8. ⟨10.23919/ECC57647.2023.10178397⟩. ⟨hal-04536968⟩