{"id":"W4386131897","doi":"10.1109/ojcsys.2023.3308009","title":"Global Performance Guarantees for Localized Model Predictive Control","year":2023,"lang":"en","type":"article","venue":"IEEE Open Journal of Control Systems","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Leverage (statistics); Computer science; Model predictive control; Node (physics); Telecommunications network; Distributed computing; Control (management); Mathematical optimization; Mathematics; Computer network; Engineering; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001796588,0.001327347,0.001028243,0.0004583417,0.0007199215,0.001183159,0.0009321648,0.0008638926,0.003220159],"category_scores_gemma":[0.007777654,0.0004045341,0.0005167875,0.0004675738,0.001475595,0.001816918,0.002255793,0.002061575,0.0005676273],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000859061,"about_ca_system_score_gemma":0.001165064,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001451622,"about_ca_topic_score_gemma":0.001268711,"domain_scores_codex":[0.9988887,0.0002843951,0.00004118482,0.0002123543,0.0003971058,0.0001762833],"domain_scores_gemma":[0.9955772,0.002650206,0.000434452,0.0005705274,0.0006408932,0.0001266404],"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":[0.00008298453,0.00002870476,0.0003958608,0.0001148221,0.00001732195,0.0000587036,0.00007660278,0.9435101,0.005205853,0.03535346,0.001027709,0.01412797],"study_design_scores_gemma":[0.000009322617,0.00005865154,0.0001542069,0.00001625414,0.000006958022,0.00001407238,0.00002547637,0.9791564,0.002001971,0.01806976,0.0004800035,0.000007025299],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02621487,0.0003318875,0.9628701,0.0003416629,0.00002775203,0.00002650436,0.0000687362,0.0005372071,0.009581277],"genre_scores_gemma":[0.9714145,0.0003404346,0.02616499,0.000124414,0.00005058738,0.0001097135,0.0001009098,0.0001466757,0.001547761],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003220159,"threshold_uncertainty_score":0.01077253,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01415672603174581,"score_gpt":0.2566035077969619,"score_spread":0.2424467817652161,"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."}}