{"id":"W3006321946","doi":"10.1109/tcyb.2019.2963141","title":"Integral-Type Event-Triggered Model Predictive Control of Nonlinear Systems With Additive Disturbance","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Cybernetics","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":85,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council; University of Victoria","keywords":"Control theory (sociology); Model predictive control; Robustness (evolution); Nonlinear system; Rendering (computer graphics); Contraction (grammar); Computer science; Mathematics; Constraint (computer-aided design); Mathematical optimization; Control (management); Artificial intelligence","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":[0.000751548,0.000707254,0.0008165789,0.0002721697,0.00031369,0.000971528,0.001301413,0.0005737695,0.001065777],"category_scores_gemma":[0.001203094,0.0002604284,0.0004873405,0.0004180546,0.0006697493,0.000741848,0.0008515246,0.001171023,0.0001072418],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004398987,"about_ca_system_score_gemma":0.0005653945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002153381,"about_ca_topic_score_gemma":0.001342727,"domain_scores_codex":[0.9995964,0.00007063662,0.00001948156,0.00007866855,0.0001716806,0.00006316591],"domain_scores_gemma":[0.9995409,0.0002190397,0.00009205689,0.00004182686,0.00007986045,0.00002630481],"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":[0.0001648919,0.00005197146,0.0002572937,0.0001524492,0.00003512291,0.000165503,0.00008713315,0.9425691,0.006081648,0.01335885,0.0003918332,0.03668409],"study_design_scores_gemma":[0.00000466433,0.00002562076,0.00003888775,0.000002361379,0.000003331088,0.00000911559,0.000002153045,0.9981951,0.0007788008,0.0007823054,0.0001554973,0.00000213901],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01835993,0.0002344796,0.9784012,0.00004938541,0.00006763768,0.00002224822,0.00001842117,0.0002461814,0.002600427],"genre_scores_gemma":[0.9778241,0.0001836064,0.02069998,0.00003754219,0.0000293861,0.00003052874,0.00003267083,0.0000170375,0.001145245],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002153381,"threshold_uncertainty_score":0.0042817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008332796021869343,"score_gpt":0.2000219770158134,"score_spread":0.191689180993944,"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."}}