{"id":"W2018375358","doi":"10.1109/iecon.2012.6388707","title":"High order robust Terminal Iterative Learning Control design using Genetic Algorithm","year":2012,"lang":"en","type":"article","venue":"","topic":"Iterative Learning Control Systems","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Robustness (evolution); Algorithm; Thermoforming; Iterative learning control; Computer science; Terminal (telecommunication); Robust control; Temperature control; Control theory (sociology); Control system; Genetic algorithm; Artificial intelligence; Control (management); Engineering; Control engineering; Machine learning; Mechanical engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003853263,0.0003112862,0.0003688639,0.0001368534,0.0001751528,0.0001417122,0.0001183412,0.0001224645,0.0003047789],"category_scores_gemma":[0.00006241829,0.000283232,0.00006077903,0.0002165392,0.00003473231,0.0004005902,0.00001862866,0.0003900357,0.0001762286],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001526243,"about_ca_system_score_gemma":0.00002075633,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004804835,"about_ca_topic_score_gemma":7.88665e-7,"domain_scores_codex":[0.9981857,0.0003947241,0.0003433394,0.0002009241,0.0002164064,0.0006589209],"domain_scores_gemma":[0.9992812,0.000210395,0.00006705098,0.0001645477,0.0001258781,0.0001509315],"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":[0.000006693265,0.0000158668,0.001865201,0.00001710098,0.0001110902,0.0000154703,0.0008953785,0.9802489,0.007648432,0.0000547556,0.0001094329,0.009011672],"study_design_scores_gemma":[0.0009896412,0.00006832508,0.002239344,0.00003911886,0.00004850954,0.00009203169,0.0001218484,0.9937369,0.0009218381,0.00000577371,0.001365841,0.0003708595],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02587531,0.0006604576,0.9712883,0.00001237714,0.0006714748,0.0003948454,0.00000286112,0.0004335738,0.0006608194],"genre_scores_gemma":[0.7645119,0.000002161143,0.2338366,0.00004798176,0.0007341589,0.00003450567,0.000003674701,0.0000713206,0.0007577265],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7386366,"threshold_uncertainty_score":0.999962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01686264653078356,"score_gpt":0.2197805734842816,"score_spread":0.2029179269534981,"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."}}