{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007009362,0.0007921521,0.000694754,0.000254838,0.0002260345,0.0007162904,0.0009613429,0.0007889926,0.001490276],"category_scores_gemma":[0.002315977,0.0003280281,0.0004701877,0.0003133296,0.0004783593,0.0006163427,0.00080228,0.001029518,0.0002850977],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003714598,"about_ca_system_score_gemma":0.0007030743,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001933013,"about_ca_topic_score_gemma":0.001259228,"domain_scores_codex":[0.9994368,0.0001705344,0.00003161397,0.000109426,0.0001984965,0.00005321982],"domain_scores_gemma":[0.999356,0.0003168268,0.0001152375,0.00007543468,0.0001177056,0.00001883588],"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":[0.0001199208,0.00005266771,0.0002441081,0.0001137289,0.00004474257,0.0001594236,0.00006173226,0.9179985,0.006718196,0.02132481,0.0006082683,0.05255392],"study_design_scores_gemma":[0.000006230114,0.00002769525,0.00003249593,0.000002625022,0.000002659696,0.00000565462,0.000001387803,0.9981903,0.0007347509,0.0007543194,0.0002388471,0.000003116369],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01177522,0.000304886,0.9843386,0.0001156087,0.0000631708,0.00004016315,0.00002066133,0.0003423175,0.002999438],"genre_scores_gemma":[0.9088321,0.0002724384,0.0882438,0.00008097113,0.00007402716,0.0001593888,0.00005775895,0.000049361,0.002230196],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001933013,"threshold_uncertainty_score":0.004985452,"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."}}