{"id":"W2302302208","doi":"10.1049/iet-cta.2015.0824","title":"Data‐driven optimal terminal iterative learning control with initial value dynamic compensation","year":2016,"lang":"en","type":"article","venue":"IET Control Theory and Applications","topic":"Iterative Learning Control Systems","field":"Engineering","cited_by":40,"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; Control theory (sociology); Convergence (economics); Optimal control; Process (computing); Initial value problem; Terminal (telecommunication); Compensation (psychology); Computer science; Mathematical optimization; Batch processing; Iterative method; Control (management); Mathematics; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009456474,0.0007028941,0.0006939684,0.0003518187,0.0004370227,0.0008425472,0.001118983,0.0008359198,0.0009537752],"category_scores_gemma":[0.002060912,0.0002766214,0.0004567718,0.0004187542,0.0009834653,0.0006612206,0.000947633,0.0008831991,0.0001983063],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007664582,"about_ca_system_score_gemma":0.00113966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003915088,"about_ca_topic_score_gemma":0.002577157,"domain_scores_codex":[0.9994729,0.00009884599,0.00003588315,0.000114393,0.0002087467,0.00006929691],"domain_scores_gemma":[0.9991442,0.0003436193,0.0001317943,0.00006235048,0.0002892313,0.00002873366],"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":[0.0001700931,0.00007025884,0.0005235556,0.0001705139,0.00003258689,0.0001044402,0.0001772826,0.8942984,0.01233613,0.01574074,0.0007136811,0.07566228],"study_design_scores_gemma":[0.000008923279,0.00004998177,0.0000601914,0.00000489951,0.000003017502,0.00001049463,0.000003537054,0.9965073,0.002019568,0.001036381,0.0002887917,0.000006916886],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01689676,0.0001715585,0.9794989,0.00008203374,0.00003756391,0.00004725041,0.00001288026,0.0002087372,0.003044288],"genre_scores_gemma":[0.945734,0.0001238366,0.05133452,0.00007277824,0.00001970829,0.0001362314,0.00004853905,0.00002031661,0.002509975],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003915088,"threshold_uncertainty_score":0.007784605,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006645607286933961,"score_gpt":0.246199920058668,"score_spread":0.239554312771734,"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."}}