{"id":"W4251580186","doi":"10.32920/ryerson.14662608","title":"Subspace predictive control: stability and performance enhancement","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Model predictive control; Subspace topology; Stability (learning theory); Computer science; Control theory (sociology); Controller (irrigation); Mathematical optimization; System identification; Identification (biology); Control (management); Data mining; Machine learning; Artificial intelligence; Mathematics","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":[0.0007725607,0.0006577941,0.0006130583,0.0004517824,0.0003164653,0.0008483479,0.0006620551,0.0006110898,0.001534794],"category_scores_gemma":[0.001596223,0.0001969107,0.0003592032,0.0005913207,0.0004956465,0.0008750357,0.001077592,0.0009638192,0.0003607487],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000306808,"about_ca_system_score_gemma":0.0004911202,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001322993,"about_ca_topic_score_gemma":0.0007606467,"domain_scores_codex":[0.9994723,0.0001087018,0.00002486443,0.0000919253,0.000261308,0.0000409233],"domain_scores_gemma":[0.9993906,0.0002209432,0.00007016679,0.00009706064,0.0002028742,0.00001827401],"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":[0.0001761788,0.00008957391,0.0008212617,0.000332727,0.00005720502,0.0001528952,0.0002261145,0.5067959,0.03690068,0.06689249,0.00264744,0.3849076],"study_design_scores_gemma":[0.000004913148,0.0000662598,0.0002251255,0.000009888468,0.00000614082,0.00004050878,0.000009939344,0.9883356,0.003708363,0.005612038,0.001971258,0.00001002671],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01327047,0.001726449,0.9740062,0.0002288173,0.00007626317,0.0000249281,0.00003077458,0.0004678887,0.01016822],"genre_scores_gemma":[0.8978049,0.001860168,0.09485318,0.0001333344,0.0001824701,0.00007789988,0.0001177017,0.00009155682,0.004878853],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001534794,"threshold_uncertainty_score":0.005134404,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006900384655802071,"score_gpt":0.194080158819199,"score_spread":0.187179774163397,"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."}}