{"id":"W3135662316","doi":"10.1016/j.compchemeng.2021.107276","title":"Model predictive control using subspace model identification","year":2021,"lang":"en","type":"article","venue":"Computers & Chemical Engineering","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Subspace topology; Model predictive control; Identification (biology); Representation (politics); System identification; Matrix (chemical analysis); Control theory (sociology); State-space representation; Process (computing); Mathematical optimization; Computer science; Controller (irrigation); Algorithm; Control (management); Mathematics; Artificial intelligence; Data modeling","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00006120005,0.0002304479,0.0002788046,0.00006978222,0.00003177083,0.00005520176,0.000150676,0.0001263877,0.0000012676],"category_scores_gemma":[0.00004827253,0.0002949696,0.00008563094,0.0002185427,0.00001346517,0.0002958652,0.00003965403,0.0002076331,0.000003513088],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002999751,"about_ca_system_score_gemma":0.00003033715,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":5.401737e-7,"about_ca_topic_score_gemma":9.371913e-8,"domain_scores_codex":[0.9988604,0.000007764396,0.0003338389,0.0002982957,0.0001824358,0.0003172838],"domain_scores_gemma":[0.9993637,0.00005325056,0.00004215274,0.0002974641,0.0001212539,0.0001221802],"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":[0.000002692664,0.000005473222,0.000002617931,0.00003839591,0.00003335427,0.000002669934,0.00005365856,0.6015728,0.3979916,0.0001668754,0.00002909689,0.0001007676],"study_design_scores_gemma":[0.0005432126,0.000001921029,0.000003626576,0.00006584795,0.00003411217,0.00001471278,0.000005836279,0.9538444,0.04512474,0.00009826765,0.000008388191,0.0002549814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02968382,0.0003721098,0.9686151,0.00002203196,0.0003273704,0.0001605947,0.00001568947,0.0007304845,0.00007275688],"genre_scores_gemma":[0.8824083,0.000008622549,0.1172991,0.00002247318,0.0001228127,0.00002406016,0.00002586887,0.00007011509,0.00001868734],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8527245,"threshold_uncertainty_score":0.9999502,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00866491317255864,"score_gpt":0.1955375812116998,"score_spread":0.1868726680391412,"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."}}