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oai:data.mendeley.com/krj5hfknb5.1 2023-05-02T07:19:47Z

Dutta, Rajdeep A Decentralized Learning Strategy to Restore Connectivity during Multi-agent Formation Control Mendeley Data A brief description of the contents is given below. (i) This folder contains three main script files that demonstrate the performance of our proposed approach in restoring the communication connectivity of a dynamic multi-agent network under the loss of single, double, and triple agent(s), engaged in a formation control mission. Related files: main_consensus_recovery1.m, main_consensus_recovery2.m, main_consensus_recovery3.m; (ii) Along with the main files, it contains distinct neural networks to cope with the communication loss of different agents. Related files: trainApredict1.m, trainApredict2.m, trainApredict3.m; (iii) Further, the performance of the proposed method is validated even in the presence of localization uncertainties up to some extent. Related file: main_consensus_recovery1_noise.m; (iv) Also, a connectivity function is included to calculate the state-dependent connectivity of a dynamic multi-agent system. Related file: CalcConnectivity.m Note that the data and codes contained in this folder are associated with the paper: https://doi.org/10.1016/j.neucom.2022.11.054 Artificial Neural Networks Distributed Control System Multi-Agent System Dynamic Functional Connectivity Kandath, Harikumar Jayavelu, Senthilnath Li, Xiaoli Sundaram, Suresh Pack, Daniel Dataset doi:10.17632/krj5hfknb5.1 oai:data.mendeley.com/krj5hfknb5.1 Creative Commons Attribution 4.0 International http://creativecommons.org/licenses/by/4.0 https://data.mendeley.com/datasets/krj5hfknb5 2023-05-02T07:19:47Z