{"id":"W2006583060","doi":"10.1109/acc.2010.5531423","title":"Robust design of Terminal ILC with an Internal Model Control using μ-analysis and a genetic algorithm approach","year":2010,"lang":"en","type":"article","venue":"","topic":"Iterative Learning Control Systems","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; École de Technologie Supérieure","funders":"","keywords":"Robustness (evolution); Thermoforming; Control theory (sociology); Internal model; Iterative learning control; Temperature control; Exponential function; Robust control; Terminal (telecommunication); Algorithm; Genetic algorithm; Control system; Computer science; Engineering; Control engineering; Mathematics; Control (management); Artificial intelligence; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001440101,0.0009461651,0.0008188343,0.0005239092,0.0004933209,0.001527414,0.001180857,0.001057304,0.002919684],"category_scores_gemma":[0.002414391,0.0003964361,0.0006894289,0.0004158136,0.0009830858,0.0005895105,0.0006467347,0.001279729,0.0007921269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001244676,"about_ca_system_score_gemma":0.001568898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00574331,"about_ca_topic_score_gemma":0.003169682,"domain_scores_codex":[0.9992694,0.000136702,0.00004193267,0.0001886917,0.0002685295,0.00009472464],"domain_scores_gemma":[0.998857,0.0003755969,0.0002315147,0.00008390643,0.0004188789,0.00003315374],"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":[0.0001349176,0.00006386478,0.0003466814,0.0001621309,0.00005952864,0.00007672535,0.000158291,0.8838972,0.01549848,0.01046059,0.001307357,0.08783428],"study_design_scores_gemma":[0.00001275794,0.00006032208,0.00005738193,0.000008658899,0.000008948275,0.00001317153,0.000005303199,0.9949625,0.00353736,0.000563981,0.0007626669,0.000007052039],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00588514,0.00006859153,0.9902733,0.00009230201,0.0000265198,0.00004260176,0.00001421363,0.0005570027,0.003040406],"genre_scores_gemma":[0.7442448,0.0001383724,0.2501864,0.000155269,0.00003821738,0.0003371641,0.00009595532,0.0001446251,0.00465919],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00574331,"threshold_uncertainty_score":0.01141977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02445079763636364,"score_gpt":0.2220157008353313,"score_spread":0.1975649031989676,"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."}}