{"id":"W3213516164","doi":"10.1109/iecon48115.2021.9589986","title":"Finite Control Set Model Predictive Control for Switched Reluctance Motor Drives with Reduced Torque Tracking Error","year":2021,"lang":"en","type":"article","venue":"","topic":"Multilevel Inverters and Converters","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Control theory (sociology); Switched reluctance motor; Torque; Model predictive control; Weighting; Tracking error; Direct torque control; Computer science; Compensation (psychology); Engineering; Induction motor; Control (management); Physics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004875342,0.0005409544,0.0007363927,0.0002947072,0.000322769,0.0007809589,0.001119642,0.000396021,0.001390227],"category_scores_gemma":[0.0009274539,0.0002753466,0.0004969661,0.0002866753,0.0004294739,0.0005529457,0.0003599332,0.0009356114,0.0002055416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000416436,"about_ca_system_score_gemma":0.0005160543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004522292,"about_ca_topic_score_gemma":0.00459764,"domain_scores_codex":[0.9996585,0.00006020182,0.00001690379,0.00004669922,0.0001940597,0.0000236606],"domain_scores_gemma":[0.9996308,0.0001573372,0.00006179849,0.00003505417,0.0001039831,0.0000108501],"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":[0.0001832735,0.00007991018,0.0002912944,0.000348682,0.00008064129,0.0001647866,0.0002287192,0.7925627,0.01508122,0.01432991,0.001732116,0.1749168],"study_design_scores_gemma":[0.000007880665,0.00005420459,0.00007436077,0.000006577124,0.000006317182,0.00001348964,0.000005369346,0.9969837,0.001295431,0.0008278585,0.000719672,0.00000521585],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01332124,0.0003278143,0.9833492,0.00006169,0.00008010269,0.00002366901,0.00001640191,0.0003963883,0.002423502],"genre_scores_gemma":[0.9239875,0.0002814091,0.0723998,0.00005534888,0.00004909324,0.0001147832,0.00008518861,0.00005898311,0.002967818],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004522292,"threshold_uncertainty_score":0.008991957,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01862125414719482,"score_gpt":0.2274485617672225,"score_spread":0.2088273076200277,"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."}}