{"id":"W1539056513","doi":"10.1109/iecon.2014.7048521","title":"A novel multi-loop self-tunning adaptive PI control scheme for switched reluctance motors","year":2014,"lang":"en","type":"article","venue":"","topic":"Electric Motor Design and Analysis","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Switched reluctance motor; Control theory (sociology); Controller (irrigation); Electronic speed control; Adaptive control; PID controller; Computer science; Nonlinear system; Machine control; Magnetic reluctance; Control engineering; Torque; Engineering; Control (management); Temperature control; Physics","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":[0.0002413708,0.0002106438,0.0003230475,0.0001091495,0.00007366419,0.00003001067,0.0001547731,0.0001033088,0.0000263491],"category_scores_gemma":[0.00009293791,0.0001944115,0.0001567583,0.0002644747,0.00001072572,0.0001058401,0.000007956984,0.0001373287,0.0000224822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007625048,"about_ca_system_score_gemma":0.00001393284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002431874,"about_ca_topic_score_gemma":0.00001470002,"domain_scores_codex":[0.9989653,0.00001319283,0.0002523259,0.0002532352,0.0001324215,0.0003835389],"domain_scores_gemma":[0.9993559,0.0001832915,0.00004619277,0.0002169116,0.00008926747,0.0001085032],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009685738,0.0002306499,0.001201103,0.0001569136,0.001355396,0.000002402874,0.0004516336,0.01105121,0.9532034,0.007601478,0.002297736,0.02235116],"study_design_scores_gemma":[0.002127827,0.0000710813,0.0002027687,0.00001519982,0.00007344686,0.000001478458,0.00001635609,0.9908446,0.005043993,0.00004713137,0.001292494,0.0002636327],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01179999,0.0001545729,0.9856128,0.00004125475,0.00007616339,0.0003560814,0.000004922082,0.0006757759,0.001278423],"genre_scores_gemma":[0.6719302,0.00000971825,0.3265261,0.0001358595,0.000121803,0.0001222937,0.000003045333,0.0000479887,0.001102978],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9797934,"threshold_uncertainty_score":0.7927873,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01527640547950729,"score_gpt":0.2150383580828116,"score_spread":0.1997619526033043,"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."}}