{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003319821,0.0005759664,0.0008757723,0.0003410174,0.0004044684,0.000700434,0.0005566202,0.0004914423,0.001775151],"category_scores_gemma":[0.001115811,0.0003378783,0.0005707116,0.0006860113,0.0003767574,0.001214131,0.0007674536,0.0009031395,0.0005970587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000246748,"about_ca_system_score_gemma":0.0006544801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003599943,"about_ca_topic_score_gemma":0.0030983,"domain_scores_codex":[0.9997445,0.00007661841,0.0000121826,0.0000472837,0.00009584648,0.00002361775],"domain_scores_gemma":[0.9996139,0.0001390546,0.00003386502,0.00007216803,0.0001296198,0.00001141751],"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":[0.0001117629,0.00007084294,0.0003096846,0.0001513988,0.00008610193,0.00004066543,0.00005855598,0.809204,0.007390407,0.01517426,0.002257987,0.1651443],"study_design_scores_gemma":[0.000002916373,0.0000160438,0.00006096383,0.000002053013,0.000003387065,0.000005532426,0.000002751635,0.9962334,0.0007664172,0.00250951,0.0003932624,0.000003743973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009316513,0.0002728261,0.986815,0.00009182646,0.00009354315,0.00001870736,0.00005504911,0.000576315,0.002760235],"genre_scores_gemma":[0.8592956,0.0005427388,0.1342483,0.00008350615,0.00006738579,0.0001554758,0.0003842968,0.0001299277,0.005092633],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003599943,"threshold_uncertainty_score":0.007157981,"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."}}