{"id":"W2146980256","doi":"10.1109/cca.2005.1507161","title":"Robust iterative learning control design via μ-synthesis","year":2005,"lang":"en","type":"article","venue":"","topic":"Iterative Learning Control Systems","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; Lakehead University","funders":"","keywords":"Iterative learning control; Control theory (sociology); Weighting; Robust control; Computer science; Robustness (evolution); Iterative method; Robot manipulator; Linear system; Iterative design; Design methods; Control engineering; Control system; Control (management); Mathematical optimization; Mathematics; Engineering; Algorithm; Artificial intelligence","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":[0.0007245858,0.0005124061,0.0005051839,0.0002255348,0.0002471702,0.0008093945,0.0008858652,0.0005568951,0.002348052],"category_scores_gemma":[0.001412545,0.0002238919,0.0003735238,0.0002473991,0.0004730629,0.0004821394,0.0005359813,0.0006569959,0.0008715118],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003139108,"about_ca_system_score_gemma":0.0006201572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000947647,"about_ca_topic_score_gemma":0.0007675092,"domain_scores_codex":[0.999604,0.00007626533,0.00003207975,0.00008164008,0.0001713391,0.00003465893],"domain_scores_gemma":[0.999571,0.0001519485,0.00007807789,0.00005221081,0.0001366733,0.00001008575],"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":[0.00008582418,0.00005648141,0.0002318838,0.0003026115,0.00004404451,0.0001021676,0.0001966419,0.6745207,0.03347366,0.04278332,0.001415845,0.246787],"study_design_scores_gemma":[0.0000192623,0.0001031411,0.00006210303,0.00002088199,0.000009143771,0.00004358232,0.000009810638,0.9801013,0.01187056,0.004302338,0.003447014,0.00001081942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002497444,0.00007544613,0.9942967,0.00003337898,0.00001591958,0.00002670754,0.000008904315,0.000322545,0.002722924],"genre_scores_gemma":[0.5132733,0.0002859067,0.4808978,0.0001164525,0.0000506475,0.0004281585,0.00009550381,0.00009610537,0.004756182],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002348052,"threshold_uncertainty_score":0.007855058,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02207619495503359,"score_gpt":0.2038220236392975,"score_spread":0.1817458286842639,"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."}}