{"id":"W3049161396","doi":"10.1002/rnc.5133","title":"Event‐triggered model predictive control for disturbed linear systems under two‐channel transmissions","year":2020,"lang":"en","type":"article","venue":"International Journal of Robust and Nonlinear Control","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Control theory (sociology); Model predictive control; Controller (irrigation); Computer science; Transmission (telecommunications); Channel (broadcasting); Trajectory; Event (particle physics); Bounded function; Control channel; Linear system; Stability (learning theory); Interval (graph theory); Control (management); Networked control system; Control system; Mathematics; Engineering; Telecommunications; Artificial intelligence","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":[0.0008746465,0.0007125373,0.0007510193,0.0002200988,0.0003455952,0.001017986,0.0007982723,0.0006913139,0.0009649253],"category_scores_gemma":[0.001752714,0.0003117759,0.0004181385,0.0003544331,0.0008507221,0.0007555417,0.0006757025,0.0009057545,0.00007693475],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005938134,"about_ca_system_score_gemma":0.0007333743,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005795171,"about_ca_topic_score_gemma":0.00260553,"domain_scores_codex":[0.9995142,0.0001419239,0.00001857462,0.00008919176,0.0001514722,0.0000846602],"domain_scores_gemma":[0.9988985,0.000650483,0.0001751806,0.00005746648,0.0001829571,0.00003551357],"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":[0.000104327,0.00002076951,0.0001351626,0.00005024816,0.00001317387,0.0001048468,0.00003791965,0.9908904,0.001635503,0.003660984,0.0001239068,0.003222729],"study_design_scores_gemma":[0.000006493492,0.00001825635,0.00005401255,0.000001157433,0.000002132734,0.000003178007,0.000003780774,0.9990809,0.0002545866,0.0005398695,0.0000338325,0.000001872853],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1002332,0.0002598311,0.8955187,0.0001997216,0.00007212168,0.00004762608,0.00005830653,0.0002127202,0.003397663],"genre_scores_gemma":[0.9957855,0.00005832087,0.003272875,0.00001319296,0.000009481569,0.00002922618,0.00001812709,0.000005847456,0.0008073561],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005795171,"threshold_uncertainty_score":0.01152289,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01701919589528344,"score_gpt":0.2506473250854941,"score_spread":0.2336281291902107,"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."}}