{"id":"W2626786463","doi":"10.1002/asjc.1569","title":"Computationally‐Light Non‐Lifted Data‐Driven Norm‐Optimal Iterative Learning Control","year":2017,"lang":"en","type":"article","venue":"Asian Journal of Control","topic":"Iterative Learning Control Systems","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China; Alberta Innovates - Technology Futures","keywords":"Iterative learning control; Linearization; Nonlinear system; Computer science; Monotonic function; Norm (philosophy); Convergence (economics); Computational complexity theory; Representation (politics); Mathematical optimization; Iterative method; Control theory (sociology); Process (computing); Mathematics; Algorithm; Control (management); Artificial intelligence; Law","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0009261664,0.0003528037,0.0008901022,0.0002329654,0.0005086928,0.0006866687,0.001241104,0.0001365237,0.00006536361],"category_scores_gemma":[0.0005828799,0.0003137791,0.0002040046,0.00006888044,0.00009229245,0.001480758,0.00005410676,0.0009924547,0.00009679001],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001140374,"about_ca_system_score_gemma":0.0001156396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000367197,"about_ca_topic_score_gemma":0.000008922858,"domain_scores_codex":[0.9975069,0.0003195384,0.0009362421,0.0002544087,0.0005327055,0.0004502212],"domain_scores_gemma":[0.9972754,0.0002319137,0.001018789,0.0006164198,0.0006185581,0.0002388984],"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":[0.0005928563,0.00007423203,0.02980304,0.00007248155,0.002798689,0.0006983586,0.00442288,0.9252496,0.01589007,0.0003330105,0.003763962,0.01630085],"study_design_scores_gemma":[0.01188425,0.000407343,0.04114051,0.0002804785,0.0002059556,0.0002502658,0.0002114127,0.9300084,0.0000684416,0.0000240444,0.01511687,0.0004020144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06902377,0.0007278787,0.9141483,0.00487841,0.001843021,0.0007192278,0.0001009935,0.0001492464,0.008409103],"genre_scores_gemma":[0.9967451,0.000004666286,0.001411684,0.000122138,0.00139583,0.000008652531,0.00001907338,0.00006856513,0.0002242658],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9277214,"threshold_uncertainty_score":0.9999315,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008617434754288961,"score_gpt":0.2422938658389268,"score_spread":0.2336764310846378,"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."}}