In this paper, we propose a decentralized learning algorithm to restore communication connectivity dur-ing multi-agent formation control. The time-varying connectivity profile of a mobile multi-agent system represents the dynamic information exchange capabilities among agents. While connected to the neigh-bors, each mobile agent in the proposed scheme learns to raise the team connectivity. When the inter -agent communication is lost, the associated trained neural network generates appropriate control actions to restore connectivity. The proposed learning technique leverages an adaptive control formalism, wherein a neural network tries to mimic the negative gradient of a value that relies on the agent-to -neighbor distances. All agents use the conventional consensus protocol during the connected multi -agent dynamics, and under communication loss, only the lost agent executes the neural network pre-dicted actions to come back to the fleet. Simulation results demonstrate the effectiveness of our proposed approach for single/multiple agent loss even in the presence of velocity disturbances.(c) 2022 Elsevier B.V. All rights reserved.
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