{"id":"W2321944329","doi":"10.1021/ie900309s","title":"Methodology for Designing and Comparing Robust Linear versus Gain-Scheduled Model Predictive Controllers","year":2009,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Control theory (sociology); Model predictive control; Robustness (evolution); Gain scheduling; Nonlinear system; Computer science; Robust control; Linear model; Norm (philosophy); Linear system; Mathematical optimization; Mathematics; Control (management)","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":[0.00441477,0.001461772,0.001207664,0.001218512,0.0004445191,0.001334534,0.00210575,0.0008796268,0.003009721],"category_scores_gemma":[0.006046126,0.0004485537,0.0009994574,0.0007406542,0.0006894207,0.0008575064,0.001156427,0.001164145,0.0005609136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008015896,"about_ca_system_score_gemma":0.0014191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001586397,"about_ca_topic_score_gemma":0.0007689653,"domain_scores_codex":[0.9981213,0.0006533281,0.000123407,0.0002170993,0.0007861042,0.00009871637],"domain_scores_gemma":[0.997951,0.001136234,0.0002389844,0.0002178441,0.0004247705,0.00003113764],"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":[0.0001231779,0.0002027646,0.0003933465,0.0005792836,0.0001689612,0.0001672705,0.00008006989,0.7755033,0.01308276,0.06724972,0.0008303455,0.1416189],"study_design_scores_gemma":[0.00003649583,0.0002978147,0.0001384968,0.0000234947,0.00003933433,0.00004512043,0.00001656697,0.9809797,0.005344843,0.01100144,0.002056629,0.00002001739],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001160065,0.0001149043,0.9972909,0.00001459509,0.00001252131,0.0001360127,0.00002368057,0.0002237449,0.00102364],"genre_scores_gemma":[0.1925055,0.0003306769,0.8043225,0.00006521147,0.00004794111,0.001280441,0.0001619037,0.000105703,0.001179987],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00441477,"threshold_uncertainty_score":0.0233478,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2819613408550644,"score_gpt":0.3690569396896455,"score_spread":0.08709559883458118,"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."}}