{"id":"W4388918986","doi":"10.1016/j.ifacol.2023.10.1295","title":"Stability and Robustness of Distributed Suboptimal Model Predictive Control","year":2023,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Model predictive control; Robustness (evolution); Computer science; Distributed element model; Sampling (signal processing); Control theory (sociology); Stability (learning theory); Mathematical optimization; Optimal control; Wireless; Scale (ratio); Control (management); Distributed computing; Mathematics; Engineering; Artificial intelligence; Machine learning","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":[0.001670117,0.0007893664,0.0008518663,0.0003596867,0.000615667,0.001220353,0.0009827961,0.000859095,0.001328046],"category_scores_gemma":[0.007832583,0.0002735701,0.0005828016,0.0002963648,0.001911954,0.000972777,0.001779296,0.0008843215,0.0002399544],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001277321,"about_ca_system_score_gemma":0.001084951,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003062213,"about_ca_topic_score_gemma":0.0008323542,"domain_scores_codex":[0.9990325,0.0002408326,0.00003708357,0.0002860918,0.000283154,0.000120362],"domain_scores_gemma":[0.9973108,0.001252924,0.0005180707,0.0004207416,0.0004254374,0.00007211075],"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":[0.0001412774,0.00001859912,0.0004198792,0.00005078885,0.00002336584,0.00007254589,0.00006557551,0.9620326,0.004394476,0.02510208,0.0003467607,0.007331946],"study_design_scores_gemma":[0.000008511948,0.00002593628,0.00009528908,0.000004702691,0.000002700072,0.000008734188,0.000007660918,0.9914624,0.0007429063,0.007483614,0.000153971,0.000003540564],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0911166,0.0004149751,0.8961706,0.0005555469,0.00005817949,0.00004307675,0.00007474064,0.0004388422,0.01112734],"genre_scores_gemma":[0.9904235,0.00009046055,0.008581877,0.00004198158,0.00001588204,0.000049021,0.00003448653,0.00002584349,0.0007368902],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003062213,"threshold_uncertainty_score":0.009267688,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01059069260736992,"score_gpt":0.2119852637118578,"score_spread":0.2013945711044879,"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."}}