{"id":"W3187239961","doi":"10.1002/rnc.5712","title":"Robust adaptive model predictive control for guaranteed fast and accurate stabilization in the presence of model errors","year":2021,"lang":"en","type":"article","venue":"International Journal of Robust and Nonlinear Control","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Consejo Nacional de Ciencia y Tecnología","keywords":"Control theory (sociology); Model predictive control; Controller (irrigation); Parametric statistics; Computer science; Adaptive control; Robust control; Stability (learning theory); Reference model; Control engineering; Control (management); Control system; Mathematics; Engineering; Artificial intelligence; Machine learning","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":[0.0007943537,0.0007729215,0.0005973653,0.0003364737,0.0003655243,0.0006476303,0.0008640215,0.0005731737,0.0008767336],"category_scores_gemma":[0.001975611,0.0002618101,0.000373966,0.0003651732,0.0005676377,0.0004480176,0.00078766,0.00104482,0.0001346029],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005276665,"about_ca_system_score_gemma":0.001049331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01069813,"about_ca_topic_score_gemma":0.005476903,"domain_scores_codex":[0.9994634,0.0001109695,0.0000240669,0.0001005584,0.000233975,0.00006697445],"domain_scores_gemma":[0.9991317,0.0003995356,0.000154786,0.00008283353,0.0002127388,0.00001829266],"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":[0.00006440959,0.00003045643,0.0001899646,0.00007070907,0.00002466898,0.00008575395,0.00004824801,0.9608945,0.008343309,0.004875593,0.0005774109,0.02479507],"study_design_scores_gemma":[0.000004797831,0.00001790679,0.00004143612,0.000002001919,0.000002570329,0.000004299395,0.000001696814,0.9987234,0.0006742896,0.0003972834,0.0001286983,0.000001622347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04235218,0.0003362617,0.9529032,0.0002045723,0.00006340309,0.00003402067,0.00002467745,0.000681743,0.003399959],"genre_scores_gemma":[0.9829162,0.00009735226,0.01585257,0.00003831929,0.00002001195,0.00004723311,0.00003255862,0.00001784516,0.0009779587],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01069813,"threshold_uncertainty_score":0.02127177,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02490743765243988,"score_gpt":0.2499420387779088,"score_spread":0.2250346011254689,"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."}}