{"id":"W1993910494","doi":"10.3182/20050703-6-cz-1902.00932","title":"REJECTION OF UNMEASUREABLE EXTENDED CONSTANT DISTURBANCES USING MODEL PREDICTIVE CONTROL","year":2005,"lang":"en","type":"article","venue":"IFAC Proceedings Volumes","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Constant (computer programming); Model predictive control; Control theory (sociology); Control (management); Computer science; Mathematics; Engineering; 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":[],"consensus_categories":[],"category_scores_codex":[0.0001568884,0.0002027865,0.0003454268,0.0001123437,0.00008231738,0.00003449905,0.0001107166,0.0001074329,0.000005349801],"category_scores_gemma":[0.00007653761,0.0002098287,0.0000643878,0.0002161741,0.00007068641,0.0008316928,0.00001190906,0.0001264054,0.000002608754],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002667944,"about_ca_system_score_gemma":0.00003164308,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001344228,"about_ca_topic_score_gemma":0.000005578541,"domain_scores_codex":[0.9988045,0.000005564758,0.0004101371,0.0002324478,0.0002652878,0.0002820888],"domain_scores_gemma":[0.9993331,0.0000195884,0.0001687386,0.00008982165,0.0003238661,0.0000648577],"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":[0.00006173609,0.00002080411,0.001300245,0.00008272984,0.00007232628,1.73246e-7,0.0003080287,0.9453651,0.05048616,0.0007391445,0.0001178686,0.001445674],"study_design_scores_gemma":[0.0008751545,0.00004114174,0.0001803897,0.0001066565,0.00006145006,0.00000858274,0.0002233484,0.9909651,0.006592544,0.0006263534,0.0001212341,0.0001979933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1091593,0.001724489,0.878439,0.00003443444,0.0002042039,0.0006606885,0.00004828442,0.0006217489,0.009107827],"genre_scores_gemma":[0.983409,0.00004640541,0.01616493,0.00001123415,0.0001480041,0.00004938183,0.000002345467,0.00004443285,0.0001241916],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8742498,"threshold_uncertainty_score":0.8556567,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009034361930390897,"score_gpt":0.2125318600918679,"score_spread":0.203497498161477,"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."}}