{"id":"W3182775342","doi":"10.1177/01423312211024006","title":"Adaptive consensus for heterogeneous unknown nonlinear multi-agent systems with asymmetric input dead-zone: A finite-time approach","year":2021,"lang":"en","type":"article","venue":"Transactions of the Institute of Measurement and Control","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Control theory (sociology); Nonlinear system; Multi-agent system; Artificial neural network; Dead zone; Lyapunov function; Consensus; Computer science; Mathematics; Adaptive control; Lyapunov stability; Imperfect; Artificial intelligence; Control (management)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009549787,0.000713509,0.0006470853,0.0004245295,0.000545022,0.0007798167,0.001238185,0.0008869117,0.0008286625],"category_scores_gemma":[0.001663644,0.0002259416,0.0006534701,0.000325823,0.001025242,0.0009998621,0.001034465,0.0008982813,0.00008855852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007473254,"about_ca_system_score_gemma":0.000570559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003258744,"about_ca_topic_score_gemma":0.001894549,"domain_scores_codex":[0.9995609,0.0001307954,0.00002534289,0.00009914056,0.0001408875,0.00004288844],"domain_scores_gemma":[0.999202,0.0004078541,0.0001314852,0.00005569038,0.0001651115,0.00003786962],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004696236,0.00001712627,0.0003023439,0.00007774666,0.00003255134,0.0001534487,0.0001167237,0.9634195,0.003611221,0.01988959,0.0001531524,0.01217973],"study_design_scores_gemma":[0.000002453124,0.00001775641,0.00003221029,0.000002211576,0.000004257234,0.0000072863,0.000008280407,0.9975025,0.0002670157,0.002031507,0.0001221135,0.000002415221],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01946347,0.0001909672,0.9783295,0.0001032072,0.00002629607,0.00001956412,0.000008542857,0.00005959182,0.0017989],"genre_scores_gemma":[0.9622242,0.0002395534,0.0353696,0.00004139271,0.00002663568,0.00009023979,0.0000310122,0.00001462301,0.001962883],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003258744,"threshold_uncertainty_score":0.006479561,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03653924416721825,"score_gpt":0.2188679193582574,"score_spread":0.1823286751910392,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}