{"id":"W2044996410","doi":"10.1109/iemdc.2013.6556285","title":"Development of a nonlinear loss minimization control of an IPMSM drive with flux estimation","year":2013,"lang":"en","type":"article","venue":"","topic":"Sensorless Control of Electric Motors","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Control theory (sociology); Stator; Flux linkage; Nonlinear system; Minification; Vector control; Computer science; Lyapunov stability; Observer (physics); Machine control; Copper loss; Control engineering; Engineering; Direct torque control; Induction motor; Control (management); Physics","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":[0.0003983378,0.0004305922,0.0003529447,0.0002224132,0.0003165443,0.0004856701,0.0005993145,0.0003411755,0.0009959765],"category_scores_gemma":[0.000552061,0.0001816577,0.0002177034,0.0001409578,0.0004710334,0.0004180424,0.0003960463,0.0004250935,0.0002711642],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003483322,"about_ca_system_score_gemma":0.000563269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001837679,"about_ca_topic_score_gemma":0.001209224,"domain_scores_codex":[0.9998118,0.00002655834,0.00001245268,0.00004537849,0.00009275787,0.00001115957],"domain_scores_gemma":[0.9997925,0.00004271223,0.00004008281,0.00001902061,0.00009686106,0.000008813763],"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":[0.0002506963,0.000189076,0.001685847,0.000975828,0.00005973374,0.0002990014,0.0004426277,0.3764321,0.1896034,0.02524128,0.003189005,0.4016313],"study_design_scores_gemma":[0.00002368334,0.0002470734,0.0005245015,0.00001720817,0.00001170462,0.00005748868,0.00001224635,0.9804705,0.01357689,0.0006040183,0.004443408,0.00001116141],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01587854,0.0001789807,0.9787459,0.0001128598,0.00006464211,0.00008720566,0.00001661475,0.0003369157,0.004578445],"genre_scores_gemma":[0.8243102,0.0003425414,0.1697365,0.00008087165,0.00007157865,0.000258731,0.00007260351,0.00004559564,0.00508129],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001837679,"threshold_uncertainty_score":0.003653944,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004334962021379357,"score_gpt":0.1871339847921396,"score_spread":0.1827990227707602,"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."}}