{"id":"W2498112369","doi":"10.1109/acc.2016.7525325","title":"Fuzzy Gain Scheduling of Subspace Predictive Controller","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Subspace topology; Computer science; Model predictive control; Fuzzy logic; Scheduling (production processes); Gain scheduling; Fuzzy control system; Control theory (sociology); Data mining; Mathematical optimization; Artificial intelligence; Control (management); Mathematics","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":[0.0004826399,0.0005732971,0.0005167237,0.0003292387,0.0003989962,0.000548052,0.0007769516,0.0005342163,0.001479776],"category_scores_gemma":[0.001123525,0.0002127822,0.0003094442,0.000306477,0.0004574692,0.000470879,0.0004243924,0.0005662652,0.0002529033],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004426869,"about_ca_system_score_gemma":0.0008870203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006076728,"about_ca_topic_score_gemma":0.003771694,"domain_scores_codex":[0.9996529,0.000064867,0.00001953819,0.00006984426,0.0001522159,0.00004073164],"domain_scores_gemma":[0.9996566,0.0001050087,0.00003909058,0.00003921496,0.0001401446,0.00002000274],"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":[0.0001454592,0.00004958835,0.0003979783,0.00009647531,0.00002496585,0.00009802185,0.0001134017,0.7875753,0.01337566,0.009634268,0.001361229,0.1871276],"study_design_scores_gemma":[0.000006965057,0.00002520712,0.00004924505,0.000002825823,0.000002662662,0.00001043166,0.000003481912,0.9975402,0.001244274,0.0007681961,0.000342711,0.000003665881],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0146393,0.0002060699,0.9824854,0.0000638924,0.00005533631,0.00003506769,0.00001683409,0.0002170837,0.002280904],"genre_scores_gemma":[0.8846177,0.0001641358,0.1130089,0.000067965,0.0000530831,0.00009439237,0.0000469105,0.00002522795,0.00192171],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006076728,"threshold_uncertainty_score":0.0120827,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004862494621330989,"score_gpt":0.1934741049537283,"score_spread":0.1886116103323973,"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."}}