{"id":"W4251095988","doi":"10.32920/ryerson.14662608.v1","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; Computer science; Stability (learning theory); Control theory (sociology); Controller (irrigation); Mathematical optimization; System identification; Control (management); Data mining; Machine learning; Mathematics; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001653904,0.0002771889,0.0004163514,0.00004095942,0.00003782999,0.00006991744,0.00009798806,0.0001955241,0.0001012216],"category_scores_gemma":[0.00002808324,0.0002845191,0.00004756836,0.00005606743,0.00003228162,0.000170457,0.0001299284,0.000347514,0.000003256049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002292159,"about_ca_system_score_gemma":0.00003886321,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000142711,"about_ca_topic_score_gemma":0.00002256985,"domain_scores_codex":[0.9988351,0.00004355402,0.0003170086,0.0003931634,0.0001809948,0.0002301386],"domain_scores_gemma":[0.9992299,0.00005105374,0.00006654067,0.0004292894,0.00014485,0.0000783837],"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":[0.00004578681,0.0000223078,0.003663566,0.000956509,0.0002266459,0.000002185025,0.0005608079,0.9897416,0.002192415,0.00003812261,0.00003844325,0.002511631],"study_design_scores_gemma":[0.000566988,0.00003813122,0.00271205,0.0001372929,0.00004317101,0.000001738991,0.000126471,0.990591,0.005407213,0.00002389947,0.00007300438,0.0002790098],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2322309,0.001729906,0.7597749,0.00003163869,0.0005143193,0.0006924902,0.00001495411,0.0003378554,0.004673115],"genre_scores_gemma":[0.9935352,0.0007773236,0.005121976,0.00001855538,0.00007916675,0.0002708898,0.0000426749,0.00003845521,0.0001158108],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7613043,"threshold_uncertainty_score":0.9999607,"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."}}