{"id":"W2329527513","doi":"10.2514/6.2007-6471","title":"An Iterative Learning Control Algorithm for Simulator Motion System Control","year":2007,"lang":"en","type":"article","venue":"AIAA Modeling and Simulation Technologies Conference and Exhibit","topic":"Iterative Learning Control Systems","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Iterative learning control; Motion control; Control (management); Motion (physics); Algorithm; Control system; Iterative method; Simulation; Artificial intelligence; Engineering; Robot","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":[0.001304805,0.0009875651,0.0008481971,0.0005097465,0.0005617304,0.0007954055,0.001588938,0.001160116,0.003013034],"category_scores_gemma":[0.002904479,0.0003963352,0.0005374293,0.0004462288,0.0009404128,0.0006768091,0.001200773,0.001420409,0.000701767],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009934399,"about_ca_system_score_gemma":0.001437197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008059964,"about_ca_topic_score_gemma":0.005245715,"domain_scores_codex":[0.9993634,0.0001538833,0.00003892532,0.0001193961,0.0002452339,0.00007920963],"domain_scores_gemma":[0.9990888,0.0004335193,0.00009881752,0.00005608025,0.0002831265,0.00003971311],"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":[0.0001032979,0.00006837065,0.0003482518,0.00007662819,0.00003451404,0.00005709537,0.0001472395,0.8292575,0.00312496,0.0100722,0.001158298,0.1555516],"study_design_scores_gemma":[0.00001372795,0.00005068119,0.00003225486,0.000004785564,0.000003063725,0.00001111821,0.000003312379,0.9978318,0.0004337982,0.001015757,0.0005950562,0.000004567022],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003788759,0.0001131899,0.9937096,0.00005876779,0.00002853394,0.0000555689,0.000006193661,0.0003891498,0.00185029],"genre_scores_gemma":[0.6143807,0.0002522445,0.3774549,0.0001797684,0.00007202929,0.0007030678,0.00009273173,0.0001011818,0.006763306],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008059964,"threshold_uncertainty_score":0.01602614,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01345303226479632,"score_gpt":0.2467971957767426,"score_spread":0.2333441635119462,"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."}}