{"id":"W4324359512","doi":"10.3390/drones7030197","title":"Finite-Time Adaptive Consensus Tracking Control Based on Barrier Function and Cascaded High-Gain Observer","year":2023,"lang":"en","type":"article","venue":"Drones","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Science Foundation of Sichuan Province; China Aerodynamics Research and Development Center","keywords":"Control theory (sociology); Multi-agent system; Computer science; Nonlinear system; Consensus; Lyapunov function; Observer (physics); Adaptive control; Control (management); Artificial intelligence","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":[0.0004548353,0.0005502394,0.00058635,0.0002790039,0.0003470955,0.0005775,0.001080316,0.0005934374,0.0007816714],"category_scores_gemma":[0.0006791299,0.0002410282,0.0004725825,0.0002432651,0.0005063164,0.0009781217,0.0006689774,0.0008798375,0.000123522],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004416517,"about_ca_system_score_gemma":0.0005641844,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0031855,"about_ca_topic_score_gemma":0.001817559,"domain_scores_codex":[0.9997274,0.00004533045,0.00001756614,0.00008946843,0.00009308961,0.00002714946],"domain_scores_gemma":[0.9997017,0.00008715262,0.00006921586,0.00003051891,0.00008956259,0.00002181676],"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":[0.0001673895,0.00008517747,0.0009373024,0.0002425911,0.000081101,0.0002769444,0.0002262026,0.8766468,0.04664193,0.03044842,0.0007365597,0.04350961],"study_design_scores_gemma":[0.0000113312,0.00006226201,0.0001160092,0.000003447183,0.000006488964,0.00001709438,0.000007184656,0.9966148,0.001709323,0.001132572,0.0003142455,0.000005265328],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02737913,0.0002297554,0.9701951,0.00006605084,0.00004386795,0.00002762211,0.00001102175,0.0001685294,0.001879002],"genre_scores_gemma":[0.9623035,0.0002753279,0.03532175,0.00003497081,0.00001940238,0.00008041879,0.0000359156,0.00001380556,0.001915106],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0031855,"threshold_uncertainty_score":0.006333947,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02394604916094553,"score_gpt":0.2211909604901005,"score_spread":0.1972449113291549,"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."}}