{"id":"W2100124461","doi":"10.1002/aic.14187","title":"Lyapunov‐based MPC with robust moving horizon estimation and its triggered implementation","year":2013,"lang":"en","type":"article","venue":"AIChE Journal","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Control theory (sociology); Computer science; Horizon; Model predictive control; Stability (learning theory); Lyapunov function; Process (computing); Nonlinear system; State (computer science); Control (management); Work (physics); Mathematical optimization; Mathematics; Engineering; Algorithm; Artificial intelligence; Machine learning; Physics","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":[0.0001975144,0.0001228683,0.0001432204,0.000105889,0.0001068806,0.0001290205,0.00005059124,0.00004841944,0.00006626682],"category_scores_gemma":[0.00002383513,0.0001050074,0.00002157174,0.0001237984,0.000006991748,0.0008517283,0.000005444794,0.0001552391,0.00001302114],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009772685,"about_ca_system_score_gemma":0.0000248628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001050034,"about_ca_topic_score_gemma":0.00001057477,"domain_scores_codex":[0.9992153,0.00004276719,0.0003105113,0.0000947941,0.0001559809,0.0001806573],"domain_scores_gemma":[0.9995314,0.00004100921,0.0001322502,0.00007351534,0.00013053,0.00009122849],"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":[0.000009475611,0.00000543926,0.0008083321,0.00002641746,0.00003818358,0.000002440397,0.00006724069,0.9280899,0.0050259,0.00002813116,0.000231691,0.06566691],"study_design_scores_gemma":[0.001561524,0.0001126862,0.004381141,0.0000513476,0.00003074906,0.00005585929,0.0001272274,0.9918717,0.001529949,0.00006729882,0.00006943071,0.0001411039],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2725366,0.0003995534,0.7262504,0.00009806306,0.0001433009,0.0003060221,0.000001467313,0.00009880935,0.0001657909],"genre_scores_gemma":[0.9640697,0.00004725975,0.03563742,0.00002004316,0.000132215,0.00002675448,0.000009356419,0.00003194731,0.00002530981],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6915331,"threshold_uncertainty_score":0.4282078,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007507463884923717,"score_gpt":0.2158853226559571,"score_spread":0.2083778587710334,"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."}}