{"id":"W1694893889","doi":"10.1109/iecon.2010.5674936","title":"Neuro-fuzzy and fuzzy logic controllers based speed control of IPMSM drive — A torque ripple optimization approach","year":2010,"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); Torque ripple; Torque; Controller (irrigation); Electronic speed control; Computer science; Fuzzy logic; Ripple; Fuzzy control system; Direct torque control; Adaptive neuro fuzzy inference system; Vector control; Control engineering; Engineering; Induction motor; Voltage; Control (management); Artificial intelligence; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002635218,0.0003403141,0.0006176485,0.0002658918,0.00005776545,0.00005300998,0.0002100494,0.0002411142,0.0001636376],"category_scores_gemma":[0.0003346656,0.0003102961,0.0001280248,0.0002622389,0.0001096606,0.00015047,0.00001516524,0.0003730152,0.000007639728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003501271,"about_ca_system_score_gemma":0.00003745338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008662448,"about_ca_topic_score_gemma":0.00004842402,"domain_scores_codex":[0.998389,0.00007295347,0.0004758199,0.0003622066,0.0002658308,0.0004341887],"domain_scores_gemma":[0.9986426,0.000461932,0.000124467,0.0004311512,0.0001483256,0.0001915164],"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":[0.0001457675,0.00007698957,0.001170025,0.00005240208,0.0001245798,0.00000237189,0.0000526199,0.856482,0.1380541,0.00282163,0.0005002375,0.0005173492],"study_design_scores_gemma":[0.006092428,0.00007684545,0.0009211774,0.000005864562,0.00009082942,0.00001079202,0.00001778889,0.9906281,0.001129213,0.0001507988,0.0005373504,0.0003388609],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.128116,0.0002314129,0.838386,0.0003698022,0.0005499987,0.001607031,0.00003332552,0.0005917348,0.03011472],"genre_scores_gemma":[0.9476358,0.0000163115,0.05162844,0.0003506195,0.0001269725,0.00003398275,0.00002517523,0.00006540768,0.0001172653],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8195199,"threshold_uncertainty_score":0.9999349,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008086251828805714,"score_gpt":0.1938939506770741,"score_spread":0.1858076988482684,"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."}}