{"id":"W2030049904","doi":"10.1109/aero.2007.352757","title":"A Novel Leader-Follower Framework for Control of Helicopter Formation","year":2007,"lang":"en","type":"article","venue":"","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Control theory (sociology); Controller (irrigation); Control (management); Gradient descent; Control engineering; Internal model; Computer science; Grid; Model predictive control; Nonlinear system; Work (physics); Function (biology); Descent (aeronautics); Engineering; Artificial intelligence; Mathematics; Artificial neural 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":[0.0002400954,0.000477635,0.0004206265,0.0001782062,0.0003862547,0.0004660202,0.001029372,0.0004755496,0.002000943],"category_scores_gemma":[0.0002251581,0.000131047,0.0003146461,0.000167595,0.0004394999,0.0004855958,0.0004827071,0.0006756252,0.0003378884],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000343247,"about_ca_system_score_gemma":0.0005090015,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001992791,"about_ca_topic_score_gemma":0.002042457,"domain_scores_codex":[0.9998447,0.00003252111,0.000006508113,0.00003653603,0.00006407407,0.0000156248],"domain_scores_gemma":[0.9999498,0.00001195305,0.00001021357,0.000006391379,0.0000137576,0.000007821119],"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":[0.00005662212,0.00008362527,0.0003005482,0.000159153,0.0000492341,0.000346103,0.0002240628,0.6068299,0.02964501,0.2560757,0.003589464,0.1026406],"study_design_scores_gemma":[0.00002636934,0.0001172706,0.0001063156,0.000009905105,0.000009340994,0.00005831995,0.00001631679,0.9627916,0.001577848,0.02421956,0.01105567,0.00001145464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002969821,0.0002754594,0.9935966,0.00006460043,0.00005582337,0.00002623395,0.00001894324,0.0001438871,0.002848663],"genre_scores_gemma":[0.5873498,0.001089788,0.3992141,0.0001650705,0.000204918,0.0003375583,0.0001141902,0.00005030278,0.01147424],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002000943,"threshold_uncertainty_score":0.00669378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02931440470571413,"score_gpt":0.2794712603783199,"score_spread":0.2501568556726058,"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."}}