{"id":"W4315630861","doi":"10.1109/tcyb.2022.3232136","title":"Data-Driven Indirect Iterative Learning Control","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Cybernetics","topic":"Iterative Learning Control Systems","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Youth Innovation Team Project for Talent Introduction and Cultivation in Universities of Shandong Province; National Natural Science Foundation of China","keywords":"Iterative learning control; Control theory (sociology); PID controller; Nonlinear system; Iterative method; Parametric statistics; Linearization; Computer science; Controller (irrigation); Linear system; Convergence (economics); Mathematics; Algorithm; Control engineering; Artificial intelligence; Control (management); 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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002406124,0.000248416,0.0003037102,0.0002729189,0.0001911653,0.0001175192,0.0003103201,0.0001269124,0.00009502497],"category_scores_gemma":[0.00001963332,0.0002583109,0.00007430153,0.0005078563,0.00004467805,0.0001860862,0.000001991048,0.0006986301,0.001365638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000865636,"about_ca_system_score_gemma":0.00001939172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009739712,"about_ca_topic_score_gemma":0.00005605828,"domain_scores_codex":[0.9985337,0.0001869702,0.0003005162,0.0003227656,0.0002728793,0.000383126],"domain_scores_gemma":[0.9989576,0.0003858674,0.00004411092,0.0004587246,0.00005945302,0.00009423692],"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":[0.00001423878,0.00001771598,0.00008719243,0.00002160359,0.0001909093,0.00002065066,0.00112347,0.9864155,0.003320752,0.00001643402,0.001261161,0.007510359],"study_design_scores_gemma":[0.0008944791,0.0001109625,0.0004800951,0.00006154571,0.00005776181,0.000007797814,0.0001121931,0.9721837,0.001765592,0.00001045407,0.02402463,0.0002907838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07278203,0.000111279,0.9151933,0.0001797662,0.002260017,0.0006222263,0.0004635852,0.003241044,0.005146741],"genre_scores_gemma":[0.99626,0.00004904793,0.0002264988,0.00004591491,0.0001488206,0.00006270464,0.00005350046,0.00008761579,0.003065869],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.923478,"threshold_uncertainty_score":0.9999869,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02442704493547824,"score_gpt":0.2502845084227912,"score_spread":0.225857463487313,"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."}}