{"id":"W2298318144","doi":"10.1109/cdc.2016.7798976","title":"Distributed iterative learning control for a team of quadrotors","year":2016,"lang":"en","type":"preprint","venue":"","topic":"Iterative Learning Control Systems","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Iterative learning control; Computer science; Trajectory; Control theory (sociology); Controller (irrigation); Stability (learning theory); Feed forward; Tracking error; Function (biology); Scalar (mathematics); Mathematical proof; Control engineering; Control (management); Artificial intelligence; Mathematics; Engineering; Machine learning","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":[0.0007136008,0.0005233128,0.0004981654,0.0002298481,0.0003842424,0.0005696426,0.0007476666,0.0004920334,0.00118563],"category_scores_gemma":[0.001492857,0.0002049948,0.0003187337,0.0002324968,0.0007951371,0.0004358964,0.001032231,0.0006026148,0.0001658812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007649489,"about_ca_system_score_gemma":0.0006635023,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006318887,"about_ca_topic_score_gemma":0.00298151,"domain_scores_codex":[0.9996467,0.00007560926,0.0000173447,0.0001218804,0.00008046231,0.0000580161],"domain_scores_gemma":[0.9993008,0.000269626,0.000152148,0.00006620172,0.0001486709,0.00006248295],"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":[0.00009798548,0.00004289618,0.0005126394,0.0000454666,0.00002199678,0.0001233685,0.00009396286,0.9693597,0.007017408,0.004669762,0.0002540362,0.01776076],"study_design_scores_gemma":[0.00001064579,0.00004579441,0.00008236409,0.000001599501,0.000002359419,0.000008801973,0.00000724137,0.9981124,0.0005512922,0.001003745,0.0001713958,0.000002330281],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09192239,0.0001955076,0.9028607,0.0002372875,0.00005666598,0.0000552441,0.00002584022,0.0002630657,0.004383292],"genre_scores_gemma":[0.9719297,0.0000562115,0.02590802,0.00002960257,0.00001062298,0.000056996,0.00001991465,0.00001161487,0.001977185],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006318887,"threshold_uncertainty_score":0.01256424,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007539381002882492,"score_gpt":0.2323966584519573,"score_spread":0.2248572774490748,"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."}}