{"id":"W4306828915","doi":"10.1016/j.automatica.2022.110638","title":"Event-triggered robust model predictive control with stochastic event verification","year":2022,"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 Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Aperiodic graph; Markov chain; Control theory (sociology); Model predictive control; Bounded function; Discrete time and continuous time; Computer science; Event (particle physics); Ergodicity; Linear matrix inequality; Mathematics; Stability (learning theory); Mathematical optimization; Control (management); 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":[0.002303348,0.001275108,0.001318048,0.0005304739,0.0003687766,0.001401152,0.001791362,0.0008607977,0.00305894],"category_scores_gemma":[0.006433806,0.0005180103,0.001175071,0.0004130625,0.0009008818,0.001328236,0.001801587,0.001689521,0.0004703269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005601253,"about_ca_system_score_gemma":0.001467554,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001824596,"about_ca_topic_score_gemma":0.001329628,"domain_scores_codex":[0.9978853,0.0005352977,0.0001087539,0.0003992904,0.0008537558,0.0002176229],"domain_scores_gemma":[0.9967629,0.001766113,0.000451682,0.0004723994,0.0004693317,0.00007760331],"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":[0.0005541479,0.0000882153,0.0003775095,0.0001620364,0.00008824256,0.0002345336,0.000049722,0.9154266,0.007115512,0.04098605,0.0008848024,0.03403271],"study_design_scores_gemma":[0.00001213983,0.00001862724,0.0000365242,0.000002898494,0.000004284064,0.000009640818,0.000001130728,0.9940239,0.001311745,0.004454096,0.0001213754,0.000003569213],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007262889,0.00005269929,0.9905556,0.00004770528,0.00004508649,0.00003257151,0.0000603416,0.0007429605,0.001200161],"genre_scores_gemma":[0.950677,0.00006873088,0.04760348,0.00006422027,0.00002996742,0.00009459414,0.0001600042,0.0001301954,0.001171863],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00305894,"threshold_uncertainty_score":0.0121814,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005606243216257154,"score_gpt":0.1896472857296972,"score_spread":0.1840410425134401,"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."}}