{"id":"W3092388874","doi":"10.1109/tie.2020.3028822","title":"Model-Free Predictive Current Control for Multilevel Voltage Source Inverters","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Electronics","topic":"Multilevel Inverters and Converters","field":"Engineering","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Control theory (sociology); Current (fluid); Generalization; Voltage; Voltage source; Controller (irrigation); Sensitivity (control systems); Sampling (signal processing); Model predictive control; Steady state (chemistry); Computer science; Constant (computer programming); Mathematics; Control (management); Electronic engineering; Engineering; Electrical engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009541871,0.000387618,0.0003923993,0.0001108672,0.0001704313,0.00005223052,0.0004182551,0.0003004757,0.00005126825],"category_scores_gemma":[0.00001952135,0.0004166053,0.0002840786,0.0001813678,0.00005552828,0.0002004971,0.000001774641,0.001022671,0.00002758033],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003341222,"about_ca_system_score_gemma":0.0001911826,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001035596,"about_ca_topic_score_gemma":0.00001612212,"domain_scores_codex":[0.9982022,0.00002942584,0.0004161265,0.0004090741,0.0002747671,0.0006683955],"domain_scores_gemma":[0.9991064,0.0001302416,0.00005945698,0.0003259336,0.00006516357,0.0003128012],"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":[0.0004940455,0.00006224135,0.000001909336,0.00003230287,0.0002155062,5.148244e-7,0.000436402,0.6583596,0.001825721,0.00004342274,0.009881056,0.3286473],"study_design_scores_gemma":[0.006476184,0.0003894775,6.738114e-7,0.00003086445,0.0001708276,0.000001120281,0.0000489665,0.9731164,0.009519458,0.0001170139,0.009729291,0.0003997206],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001423432,0.0001216355,0.9945602,0.000352882,0.001198705,0.001063273,0.0007055619,0.0005259516,0.00004833116],"genre_scores_gemma":[0.9982216,0.00009671898,0.000169211,0.0006586473,0.0003054704,0.0003567459,0.00001419578,0.0001039575,0.00007340728],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9967982,"threshold_uncertainty_score":0.9998286,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05116520506315475,"score_gpt":0.2328885035084243,"score_spread":0.1817232984452695,"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."}}