{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001675621,0.0001836048,0.0003798458,0.0001905957,0.00005288429,0.00002331936,0.0001004952,0.00006537352,0.000007709686],"category_scores_gemma":[0.00002530353,0.0001833307,0.0001064128,0.0001651463,0.00003940748,0.0002100476,0.00000446074,0.0001101212,9.90851e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000711262,"about_ca_system_score_gemma":0.00001970574,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001787956,"about_ca_topic_score_gemma":0.000007992464,"domain_scores_codex":[0.9987599,0.00003167725,0.0005311764,0.0001781056,0.0002028868,0.0002962116],"domain_scores_gemma":[0.9994277,0.0001128771,0.0002382802,0.00009317436,0.00009564692,0.00003233502],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001206079,0.0000120001,0.0005454426,0.0001421412,0.00003839445,6.643028e-7,0.00005706697,0.06467038,0.9147378,0.00001920577,0.0001575895,0.0194987],"study_design_scores_gemma":[0.006491913,0.0001520052,0.006540871,0.00005120499,0.0001384451,0.000005682894,0.00004789409,0.8812692,0.1048593,0.0001180569,0.00003500755,0.0002904591],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9800033,0.0001851474,0.01831787,0.0004878298,0.0001293651,0.0006290412,0.0000316387,0.0001460988,0.00006973754],"genre_scores_gemma":[0.9971951,0.000002306905,0.002282189,0.0001202834,0.000263905,0.0000498328,0.00002505634,0.00004743811,0.00001383903],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8165988,"threshold_uncertainty_score":0.747601,"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."}}