{"id":"W4413754725","doi":"10.1109/tpel.2025.3603415","title":"Levenberg–Marquardt Optimization-Based Fast-Convergent and Improved MTPA Control for PMSMs With Nonlinearity and Loss Compensation","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Power Electronics","topic":"Stability and Control of Uncertain Systems","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"National Natural Science Foundation of China","keywords":"Levenberg–Marquardt algorithm; Control theory (sociology); Compensation (psychology); Nonlinear system; Convergence (economics); Control (management); Mathematical optimization; Computer science; Mathematics; Physics; Economics; Artificial neural network; Psychology; Artificial intelligence","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":[0.0001598917,0.0002053745,0.0002661451,0.0001063207,0.0001635648,0.00005564921,0.00006486696,0.0001183575,0.00001512272],"category_scores_gemma":[0.000005295644,0.0001985213,0.00006362925,0.0001533663,0.0000599191,0.00009451445,3.653802e-7,0.0002029822,5.857364e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001664553,"about_ca_system_score_gemma":0.000134134,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002659053,"about_ca_topic_score_gemma":0.0003512036,"domain_scores_codex":[0.9990683,0.00003408019,0.0002492301,0.0002583572,0.0001020366,0.0002879808],"domain_scores_gemma":[0.9993419,0.000242243,0.00003644923,0.0001971781,0.0001173883,0.00006486158],"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":[0.0005416791,0.00008580395,0.00007172803,0.0001197372,0.0002139999,3.730673e-7,0.00008892461,0.9952605,0.0009203713,0.0002253082,0.00001816663,0.002453402],"study_design_scores_gemma":[0.00401642,0.0004064418,0.00007303906,0.00003647874,0.0001231819,0.000002410856,0.00004749724,0.9906972,0.003166078,0.00006566747,0.001159566,0.0002060264],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0180837,0.0004117755,0.9795351,0.0005212991,0.0002805463,0.0008583818,0.00008905899,0.0001397039,0.00008042921],"genre_scores_gemma":[0.9977619,0.00005318607,0.001655814,0.0002107362,0.00001167909,0.0001995376,0.000009592765,0.00002582814,0.00007170186],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9796782,"threshold_uncertainty_score":0.8095465,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003914890481095371,"score_gpt":0.1998645735676312,"score_spread":0.1959496830865358,"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."}}