{"id":"W2064981932","doi":"10.1007/s11063-006-9018-5","title":"Real Time Implementation of Fuzzy Gain Scheduling of PI Controller for Induction Motor Machine Control","year":2006,"lang":"en","type":"article","venue":"Neural Processing Letters","topic":"Sensorless Control of Electric Motors","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Trois-Rivières; Université du Québec","funders":"","keywords":"Control theory (sociology); Gain scheduling; Induction motor; PID controller; Fuzzy logic; Computer science; Controller (irrigation); Tracking error; Scheduling (production processes); Fuzzy control system; Control engineering; Mathematics; Control (management); Engineering; Voltage; Artificial intelligence; Mathematical optimization; Temperature control","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":[0.0003769378,0.0003152168,0.0002709598,0.000222375,0.0003150162,0.0006602734,0.0006244078,0.0003454509,0.002560035],"category_scores_gemma":[0.001147067,0.0001597502,0.0001172975,0.0001438157,0.0001899084,0.0002925768,0.0001607132,0.0005680277,0.0003631274],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003438014,"about_ca_system_score_gemma":0.0005295209,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003598599,"about_ca_topic_score_gemma":0.004352297,"domain_scores_codex":[0.9998066,0.00003150828,0.00001110539,0.00003203174,0.00008883087,0.00003001866],"domain_scores_gemma":[0.9997074,0.00009050121,0.00002765174,0.00004110238,0.0001162243,0.00001714569],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001579475,0.0004470562,0.0012438,0.0002291867,0.00006525674,0.0003040907,0.0003054824,0.2242509,0.1489316,0.01572235,0.003995133,0.6029257],"study_design_scores_gemma":[0.00006335924,0.0002805971,0.0007904471,0.000009670214,0.00001716634,0.00006355646,0.00002468316,0.9575971,0.03741956,0.001388974,0.002331452,0.00001345286],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09899072,0.0003758176,0.88978,0.0001945572,0.0003276767,0.00007782989,0.00003615831,0.001832847,0.008384224],"genre_scores_gemma":[0.9450297,0.00005237177,0.05259785,0.0000408539,0.00001973712,0.00002678207,0.00002183826,0.00002324524,0.00218753],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003598599,"threshold_uncertainty_score":0.008564234,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007324199554751004,"score_gpt":0.2326115921978201,"score_spread":0.2252873926430691,"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."}}