{"id":"W2897464708","doi":"10.1109/tpel.2018.2876607","title":"Multiple Reference Frame Based Torque Ripple Minimization for PMSM Drive Under Both Steady-State and Transient Conditions","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Power Electronics","topic":"Electric Motor Design and Analysis","field":"Engineering","cited_by":77,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Control theory (sociology); Torque ripple; Controller (irrigation); Reference frame; Stationary Reference Frame; Ripple; Torque; Transient (computer programming); Stator; Open-loop controller; PID controller; Direct torque control; Computer science; Harmonic; Engineering; Control engineering; Frame (networking); Voltage; Induction motor; Physics; Control (management); Temperature control; Electrical 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":[],"consensus_categories":[],"category_scores_codex":[0.00008413415,0.0002257991,0.0002110991,0.0002132143,0.0002500073,0.00004829101,0.00009613528,0.0001353292,0.0002005028],"category_scores_gemma":[0.000003961284,0.0002405808,0.000104532,0.0003491514,0.00007187568,0.0001295912,2.914207e-7,0.0002639825,0.00001816075],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001929136,"about_ca_system_score_gemma":0.00009035503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001503898,"about_ca_topic_score_gemma":0.0004988929,"domain_scores_codex":[0.9988226,0.0000358686,0.0002525308,0.0002986461,0.0001546166,0.0004357796],"domain_scores_gemma":[0.9993373,0.0001742953,0.00003742555,0.0002268173,0.0001103201,0.0001138372],"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":[0.0005958728,0.001129511,0.00003578687,0.0002217481,0.001544673,0.000005110137,0.00316375,0.7151141,0.2409242,0.002115419,0.007936212,0.02721367],"study_design_scores_gemma":[0.001680308,0.0009816674,0.00009248446,0.00003415378,0.0002149531,0.000004612752,0.00004836782,0.894747,0.08805133,0.0007021038,0.01293415,0.000508852],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03683539,0.0001402463,0.9616987,0.0001063491,0.0001573869,0.0003952937,0.0001874315,0.0001866194,0.0002925644],"genre_scores_gemma":[0.9957265,0.0001726621,0.003214086,0.0002058951,0.00001943174,0.0001430017,0.00003846203,0.00005252679,0.0004274925],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.958891,"threshold_uncertainty_score":0.98106,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01093848058680886,"score_gpt":0.22878741563997,"score_spread":0.2178489350531611,"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."}}