{"id":"W2236349324","doi":"10.1021/acs.iecr.5b03772","title":"Offset-Free Model Predictive Control with Explicit Performance Specification","year":2016,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Johnson Controls","keywords":"Model predictive control; Control theory (sociology); Computer science; Offset (computer science); Process (computing); Closed loop; Controller (irrigation); Internal model; Key (lock); Process control; Control (management); Mathematical optimization; Control engineering; Mathematics; Engineering; Artificial intelligence","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":[0.0008279533,0.001030654,0.0007415878,0.0002524523,0.0003245383,0.001144241,0.001304675,0.0009547448,0.001727478],"category_scores_gemma":[0.001894386,0.000430364,0.0004116659,0.0003996477,0.0006862146,0.001026242,0.001238689,0.001285746,0.0005275332],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005484996,"about_ca_system_score_gemma":0.001007298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002043148,"about_ca_topic_score_gemma":0.001760133,"domain_scores_codex":[0.9992455,0.0001258288,0.00003120079,0.0001137168,0.000402931,0.00008079041],"domain_scores_gemma":[0.9993481,0.0002940403,0.00009780339,0.00009902838,0.0001451533,0.00001587281],"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":[0.0000519915,0.00002880802,0.000108597,0.0001054448,0.0000124552,0.00006643773,0.00005289391,0.9606265,0.003855053,0.0162273,0.0003958665,0.01846851],"study_design_scores_gemma":[0.000007337886,0.00003125463,0.00004111209,0.000005352327,0.000003200016,0.00000692998,0.000002893318,0.9963508,0.0009057667,0.00225993,0.0003816633,0.000003626792],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01146307,0.0002386719,0.9807978,0.0001049118,0.00004316648,0.00003127147,0.00004016826,0.0004324144,0.006848449],"genre_scores_gemma":[0.9488975,0.0002269502,0.04502586,0.00008416976,0.00004892962,0.0001287775,0.0001052947,0.00007393658,0.005408581],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002043148,"threshold_uncertainty_score":0.005779028,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04413401909960532,"score_gpt":0.2484724499243144,"score_spread":0.2043384308247091,"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."}}