{"id":"W2571988845","doi":"10.1002/rnc.3734","title":"Robust output synchronization of linear multi‐agent systems with constant disturbances via integral control","year":2017,"lang":"en","type":"article","venue":"International Journal of Robust and Nonlinear Control","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Education and Child Care","funders":"Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Control theory (sociology); Synchronization (alternating current); Computer science; Constant (computer programming); Controller (irrigation); Protocol (science); Observer (physics); State (computer science); Control (management); Multi-agent system; Algorithm; Artificial intelligence; Computer network","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":[0.0007813415,0.0004979026,0.0005820625,0.0003300929,0.0002761516,0.0007698914,0.0006963206,0.0004648853,0.0008502375],"category_scores_gemma":[0.001550409,0.000182767,0.000424009,0.0002945626,0.0008282448,0.0006230387,0.0009578117,0.0007759461,0.0001278458],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005712387,"about_ca_system_score_gemma":0.0005850237,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002993464,"about_ca_topic_score_gemma":0.001057186,"domain_scores_codex":[0.9995868,0.0001000502,0.00002383728,0.0001178936,0.0001247125,0.0000466584],"domain_scores_gemma":[0.9993657,0.0002901738,0.0001539691,0.00006423875,0.00009969733,0.00002619586],"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":[0.00007639366,0.00002255031,0.0002747753,0.0000703171,0.00003128819,0.0001098378,0.0001267596,0.9425024,0.007636283,0.02365394,0.0003051489,0.0251902],"study_design_scores_gemma":[0.000008533027,0.00002879108,0.00005871566,0.000002310618,0.000004844906,0.000006879285,0.000008336408,0.9959413,0.000812867,0.002886108,0.0002379988,0.000003141248],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02588444,0.0001113777,0.9714372,0.00008342267,0.00003096829,0.00002465452,0.000008956139,0.0001568739,0.002262112],"genre_scores_gemma":[0.9767264,0.00009992937,0.02122211,0.00002303126,0.00002341187,0.00004594833,0.00001792306,0.00002493621,0.001816219],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002993464,"threshold_uncertainty_score":0.00595206,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0251992552285982,"score_gpt":0.2514691156728528,"score_spread":0.2262698604442546,"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."}}