{"id":"W2150581696","doi":"10.1109/cdc.1994.411195","title":"Adaptive control of variable reluctance motors using spline functions","year":2002,"lang":"en","type":"article","venue":"","topic":"Sensorless Control of Electric Motors","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Control theory (sociology); Switched reluctance motor; Torque; Commutation; Direct torque control; Rotor (electric); Adaptive control; Computer science; Controller (irrigation); Magnetic reluctance; PID controller; Control engineering; Spline (mechanical); Electromagnetic coil; Engineering; Induction motor; Control (management); Physics; Voltage; Artificial intelligence; Electrical engineering; Magnet; Mechanical engineering","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00006772799,0.0001422678,0.0002577455,0.000122363,0.00003723173,0.000007548948,0.00009055524,0.00007308999,0.000917816],"category_scores_gemma":[0.00004810668,0.000141093,0.00006038744,0.0003717387,0.00002601753,0.0001167372,0.000005524327,0.0001256846,0.00003857404],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008310242,"about_ca_system_score_gemma":0.0000068981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006030129,"about_ca_topic_score_gemma":0.000004315141,"domain_scores_codex":[0.9991724,0.00001622252,0.0002740076,0.0001388557,0.0001404585,0.0002580494],"domain_scores_gemma":[0.9994709,0.0001166621,0.0000452652,0.0002246504,0.00007851067,0.00006399076],"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":[0.00004066307,0.00008269941,0.0007752204,0.00003355163,0.0003417137,0.000006751312,0.00006840772,0.870253,0.1169658,0.005878637,0.002753507,0.002800127],"study_design_scores_gemma":[0.0008106132,0.00004772444,0.0001846146,0.00001421479,0.00004958873,0.000006356062,0.00001963313,0.9961938,0.001410515,0.00008135774,0.001037665,0.0001439455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07499224,0.001357503,0.8811544,0.00003135765,0.0004625858,0.0003426061,0.00002105261,0.0003977262,0.0412405],"genre_scores_gemma":[0.9875706,0.00001943232,0.01087669,0.00003899739,0.0001118123,0.000009077527,7.644877e-7,0.00003329812,0.001339377],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9125783,"threshold_uncertainty_score":0.9999955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01621088445191288,"score_gpt":0.1822588379298127,"score_spread":0.1660479534778998,"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."}}