{"id":"W2157813831","doi":"10.1109/icca.2009.5410349","title":"Near optimal LQR step tracking for a finite set of LTI plants","year":2009,"lang":"en","type":"article","venue":"","topic":"Iterative Learning Control Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Control theory (sociology); Nonlinear system; Tracking (education); Controller (irrigation); Set (abstract data type); Computer science; Optimal control; Finite set; Control engineering; Mathematics; Mathematical optimization; Engineering; Control (management); Artificial intelligence; Physics","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":[],"consensus_categories":[],"category_scores_codex":[0.0001345604,0.0001073068,0.0002053619,0.00004595887,0.00003375005,0.00004363347,0.00008530327,0.00005680192,0.00002976096],"category_scores_gemma":[0.00003607737,0.00009930239,0.00005776443,0.00005089784,0.000008829044,0.00009845833,0.000004082555,0.00008165374,0.00001712731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001885608,"about_ca_system_score_gemma":0.000007100312,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006801545,"about_ca_topic_score_gemma":0.000003566796,"domain_scores_codex":[0.9993971,0.00001826275,0.0002111224,0.00008536028,0.00008924939,0.0001989233],"domain_scores_gemma":[0.9996496,0.0001350211,0.00003265421,0.0001139865,0.00003570071,0.00003307476],"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":[0.0001052414,0.00002338288,0.001014013,0.0001551364,0.0001172719,0.000007870241,0.003533691,0.9219227,0.03137875,0.000873582,0.003311169,0.03755716],"study_design_scores_gemma":[0.0009331529,0.0001693825,0.002015771,0.00007882887,0.000008188033,0.000004807004,0.0001326561,0.9820205,0.003178968,0.0000162108,0.01128392,0.0001576057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5930421,0.0002057479,0.396282,0.00008617284,0.0002745577,0.0005734908,0.00006177539,0.0004020231,0.009072111],"genre_scores_gemma":[0.9929345,0.000001403144,0.006484452,0.00003450263,0.0001149316,0.000009461267,0.00001351488,0.00001810652,0.0003891476],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3998923,"threshold_uncertainty_score":0.4049434,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01702858554141463,"score_gpt":0.2452283657378246,"score_spread":0.22819978019641,"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."}}