{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000245094,0.0004560091,0.000504373,0.0002297037,0.0002508371,0.0005448467,0.0008803235,0.00034245,0.0009822592],"category_scores_gemma":[0.0006931062,0.0001571327,0.0003214528,0.0003000477,0.0002980264,0.0004200033,0.0003277558,0.000810677,0.0001859999],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003062836,"about_ca_system_score_gemma":0.000326721,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002940692,"about_ca_topic_score_gemma":0.003444048,"domain_scores_codex":[0.9997895,0.0000281689,0.000009764835,0.00003938097,0.0001169119,0.00001624504],"domain_scores_gemma":[0.9997866,0.00007149186,0.00004053843,0.00003700886,0.00005797881,0.000006566492],"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":[0.0001328919,0.0001147821,0.0006024683,0.0002963899,0.00006055671,0.0001545963,0.0001385341,0.6945313,0.02250417,0.01214036,0.002542495,0.2667815],"study_design_scores_gemma":[0.000008654715,0.00005572288,0.0001561381,0.000006491516,0.000006429663,0.00002201304,0.00000318173,0.9957857,0.001950055,0.001027685,0.0009732804,0.000004657786],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0260291,0.0007462969,0.9648609,0.0001619883,0.0001473884,0.00005432064,0.00004877558,0.001355914,0.006595302],"genre_scores_gemma":[0.9663241,0.0002345677,0.03201919,0.00005197637,0.00004631846,0.00006037915,0.00004881164,0.00002743835,0.001187208],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002940692,"threshold_uncertainty_score":0.005847156,"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."}}