{"id":"W4362467188","doi":"10.1016/j.automatica.2023.111014","title":"Spatiotemporal learning-based stochastic MPC with applications in aero-engine control","year":2023,"lang":"en","type":"article","venue":"Automatica","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Japan Society for the Promotion of Science London; Victoria University; University of Saskatchewan","keywords":"Computer science; Model predictive control; Kalman filter; Global Positioning System; Control theory (sociology); Stability (learning theory); Hyperparameter; Controller (irrigation); Mathematical optimization; Control (management); Mathematics; 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":[0.000521872,0.0005608115,0.0007449332,0.0003150255,0.0002995329,0.0005046045,0.000618574,0.0005725267,0.0007742071],"category_scores_gemma":[0.002120127,0.0002941094,0.000447617,0.0007257215,0.0005321687,0.0005369334,0.000757254,0.0008099071,0.00009721084],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004906569,"about_ca_system_score_gemma":0.0008356996,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009057049,"about_ca_topic_score_gemma":0.005094448,"domain_scores_codex":[0.9998259,0.00003972362,0.00001219468,0.00003945405,0.00006568119,0.0000171635],"domain_scores_gemma":[0.9993697,0.0003504228,0.00009244649,0.00003584452,0.0001221382,0.00002946157],"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":[0.00001927342,0.00001285329,0.0001714184,0.00003177435,0.00001402094,0.0000169737,0.0000124266,0.976004,0.0008664402,0.005885163,0.0002053028,0.01676042],"study_design_scores_gemma":[6.911263e-7,0.000003834941,0.00002571606,7.233443e-7,0.000001005886,0.000001552373,5.087446e-7,0.9991068,0.0001094977,0.0006798682,0.00006885738,9.49497e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01783446,0.0008644987,0.9791802,0.0002179721,0.00008022883,0.00001232819,0.00003977013,0.0001652578,0.001605276],"genre_scores_gemma":[0.9262423,0.001144537,0.06973899,0.00006999729,0.000123288,0.00004687922,0.00009855349,0.00006365744,0.002471719],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009057049,"threshold_uncertainty_score":0.01800865,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003688456922007439,"score_gpt":0.1954979922754582,"score_spread":0.1918095353534508,"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."}}