{"id":"W4297098385","doi":"10.2316/j.2022.201-0274","title":"SENSORLESS CONTROL OF PERMANENT MAGNET SYNCHRONOUS MOTOR BASED ON OPTIMIZATION OF NON-SINGULAR FAST TERMINAL SLIDING MODE OBSERVER, 138-144.","year":2022,"lang":"en","type":"article","venue":"Mechatronic systems and control","topic":"Iterative Learning Control Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Control theory (sociology); Terminal sliding mode; Observer (physics); Permanent magnet synchronous motor; Vector control; Terminal (telecommunication); Magnet; Position (finance); Computer science; Synchronous motor; Control engineering; Control (management); Sliding mode control; Engineering; Physics; Voltage; Induction motor; Electrical engineering; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002214738,0.0002951268,0.0003713099,0.0001180995,0.000125817,0.000372543,0.0003200742,0.000222126,0.0008308787],"category_scores_gemma":[0.0003867884,0.0001566113,0.0002229336,0.0001111029,0.0002192503,0.0003766846,0.000204085,0.0003267341,0.0001857659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001894287,"about_ca_system_score_gemma":0.0003604603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001037212,"about_ca_topic_score_gemma":0.001982349,"domain_scores_codex":[0.9999169,0.00001334656,0.000005155488,0.00001673191,0.00004037463,0.000007419338],"domain_scores_gemma":[0.9999119,0.00002028923,0.00001966125,0.000009329872,0.00003302879,0.000005762842],"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":[0.0004027911,0.0001236554,0.001637358,0.0005916971,0.00009021017,0.0001966685,0.0001690832,0.3812279,0.2689758,0.02539627,0.003115284,0.3180732],"study_design_scores_gemma":[0.0000215667,0.0001923945,0.0008149055,0.000006182735,0.00001049123,0.00003192672,0.000007797715,0.984638,0.01131397,0.001054542,0.001902011,0.000006291362],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02738011,0.0002793357,0.9689002,0.00004831735,0.00009870454,0.00003811617,0.00002329973,0.0003486648,0.002883237],"genre_scores_gemma":[0.9342968,0.0003151912,0.06077303,0.00002027231,0.00002618854,0.00005985773,0.00007339926,0.0000233762,0.004411937],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001037212,"threshold_uncertainty_score":0.002779603,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004278875687124352,"score_gpt":0.1893875245394509,"score_spread":0.1851086488523266,"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."}}