{"id":"W2896696595","doi":"10.1016/j.isatra.2018.08.027","title":"Robust control of saturating systems with Markovian packet dropouts under distributed MPC","year":2018,"lang":"en","type":"article","venue":"ISA Transactions","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Science and Technology Planning Project of Guangdong Province; Fundamental Research Funds for the Central Universities; Guangzhou Science and Technology Program key projects; Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Control theory (sociology); Model predictive control; Network packet; Dropout (neural networks); Computer science; Markov chain; Markov process; Controller (irrigation); Stability (learning theory); Control (management); Mathematics; 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":[0.001465958,0.00107746,0.001386713,0.0004386645,0.0005159907,0.001186379,0.001202615,0.000918261,0.001497756],"category_scores_gemma":[0.0036028,0.000537271,0.0005060838,0.0003607618,0.001459511,0.0009308153,0.001971076,0.001318852,0.0001619025],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001217828,"about_ca_system_score_gemma":0.001554198,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01296552,"about_ca_topic_score_gemma":0.007454069,"domain_scores_codex":[0.9994174,0.0001269372,0.00002624393,0.000130641,0.0001384307,0.0001603731],"domain_scores_gemma":[0.9981907,0.000996163,0.0003378932,0.0001064407,0.0002901482,0.00007867532],"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":[0.0001788216,0.00003222477,0.0002406896,0.00007343186,0.00003305061,0.00004484021,0.00005224862,0.9861541,0.001819399,0.004964819,0.0001877564,0.006218551],"study_design_scores_gemma":[0.00001134068,0.0000311326,0.00008620173,0.000002721232,0.000005046797,0.000002488525,0.000004158744,0.9984445,0.000339678,0.001015788,0.00005433275,0.000002682403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1465497,0.0004252159,0.8448365,0.0005193201,0.0001239348,0.00008025094,0.00009216202,0.000496189,0.006876791],"genre_scores_gemma":[0.9950648,0.0000639026,0.003396199,0.00002774216,0.00001842159,0.00003645591,0.00002287847,0.00001663929,0.001352947],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01296552,"threshold_uncertainty_score":0.02578014,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008498805728185524,"score_gpt":0.1857437553092449,"score_spread":0.1772449495810594,"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."}}