{"id":"W2080275105","doi":"10.1002/cjce.22134","title":"Lyapunov‐based offset‐free model predictive control of nonlinear process systems","year":2014,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; McMaster University","keywords":"Control theory (sociology); Model predictive control; Offset (computer science); Nonlinear system; Lyapunov function; Computer science; Control engineering; Internal model; Nonlinear model; Lyapunov redesign; Control (management); Engineering; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.0005306258,0.0005662358,0.0005335832,0.0002506079,0.0003201548,0.0007937869,0.0008338908,0.0004269848,0.001263269],"category_scores_gemma":[0.001088528,0.0002915043,0.00025189,0.0002873514,0.0005190203,0.0004905146,0.0006793889,0.0007195834,0.0002308353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004291776,"about_ca_system_score_gemma":0.0007534336,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003405367,"about_ca_topic_score_gemma":0.002510472,"domain_scores_codex":[0.9996952,0.00007246841,0.00001278875,0.00004537328,0.0001401047,0.00003408851],"domain_scores_gemma":[0.9996583,0.0001443478,0.00006408669,0.00002645253,0.00009413209,0.00001255523],"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":[0.0000947729,0.00002904506,0.0001394742,0.0001135381,0.00002589835,0.00009574038,0.0000522098,0.959715,0.007456674,0.008019238,0.0004278318,0.02383052],"study_design_scores_gemma":[0.000007818334,0.00004345961,0.00006381068,0.000003600488,0.000003314604,0.000004628774,0.000002229045,0.9979858,0.0008482481,0.0007833903,0.0002505158,0.000003200563],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06447754,0.0005876962,0.9262281,0.0002231121,0.0001343118,0.00003658071,0.00004347407,0.0004844255,0.007784707],"genre_scores_gemma":[0.988215,0.000119442,0.009617295,0.00002876395,0.0000203806,0.00003633595,0.00003038459,0.00001567282,0.001916706],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003405367,"threshold_uncertainty_score":0.006771088,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003856213699728742,"score_gpt":0.1689390078114207,"score_spread":0.165082794111692,"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."}}