{"id":"W4362647451","doi":"10.1109/pedstc57673.2023.10087133","title":"Sensorless Speed Control of SPMSM Using Disturbance Rejection Predictive Functional Control","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced DC-DC Converters","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Control theory (sociology); Model predictive control; Feed forward; PID controller; Compensation (psychology); Computer science; Control engineering; Stability (learning theory); Lyapunov function; Electronic speed control; Nonlinear system; Control (management); Engineering; Temperature control","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":[0.00008780241,0.0001450942,0.0002341576,0.000120298,0.00004358781,0.00000852641,0.00005554821,0.00006471888,0.00007625788],"category_scores_gemma":[0.00004065617,0.000146626,0.00007440679,0.00031542,0.00004989128,0.0001819109,0.000009896396,0.0001197739,0.00004384452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001286222,"about_ca_system_score_gemma":0.00001437888,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002051181,"about_ca_topic_score_gemma":0.000006464099,"domain_scores_codex":[0.9991249,0.00002159078,0.0002402809,0.000179755,0.0002015313,0.0002319124],"domain_scores_gemma":[0.9995109,0.0001385247,0.00004787415,0.0001642334,0.00008470889,0.00005368839],"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":[0.0002174565,0.00001741488,0.008052896,0.00006750433,0.0002465688,0.00001022623,0.0001083024,0.821428,0.1632626,0.0004216034,0.000711744,0.005455712],"study_design_scores_gemma":[0.001619782,0.00002249967,0.03077105,0.00002181019,0.00003844257,0.000006634328,0.000124491,0.9644638,0.002452506,0.0001430452,0.0001962151,0.0001396997],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1295673,0.00004686091,0.8678174,0.00003748248,0.0008558395,0.000241452,0.0000571742,0.0006049811,0.0007714986],"genre_scores_gemma":[0.9991663,0.000008198284,0.0002350441,0.00003701407,0.0001583035,0.000008107366,0.00001113859,0.00003426084,0.0003416],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.869599,"threshold_uncertainty_score":0.5979235,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01234289438413505,"score_gpt":0.210488054864478,"score_spread":0.198145160480343,"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."}}