{"id":"W2005798974","doi":"10.1109/tie.2013.2261039","title":"Network-Based Predictive Control for Constrained Nonlinear Systems With Two-Channel Packet Dropouts","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Electronics","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":151,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Control theory (sociology); Network packet; Model predictive control; Dropout (neural networks); Computer science; Networked control system; Controller (irrigation); Nonlinear system; Transmission delay; Actuator; Stability (learning theory); Transmission (telecommunications); Lyapunov function; Channel (broadcasting); Control system; Engineering; Control (management); Computer network; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000195539,0.0003946322,0.0005062819,0.000139025,0.0002204593,0.0001016058,0.0001549773,0.0003381269,0.00002024122],"category_scores_gemma":[0.00001239456,0.0003723468,0.0001175184,0.000360449,0.00006966509,0.0002758378,3.149759e-7,0.0006396858,0.0000163419],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004935229,"about_ca_system_score_gemma":0.0002810866,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004476927,"about_ca_topic_score_gemma":0.00005205328,"domain_scores_codex":[0.9979907,0.00008974058,0.000505827,0.0003437162,0.0002656168,0.0008044305],"domain_scores_gemma":[0.9987328,0.0003756392,0.0001361213,0.0003094096,0.0002751394,0.0001708777],"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":[0.0005377566,0.00005229637,0.000002095765,0.00002113829,0.0002776944,0.000001000033,0.00001879476,0.9967955,0.000346843,0.00009145786,0.0003817254,0.001473679],"study_design_scores_gemma":[0.01171091,0.001137428,3.455141e-7,0.0001074572,0.0001818797,0.000009239971,0.00005292534,0.9845337,0.001284517,0.00007997204,0.0005245062,0.0003771182],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00105652,0.0001569061,0.9923081,0.0001405737,0.001286881,0.003952855,0.0002974698,0.0006560005,0.0001446721],"genre_scores_gemma":[0.9956699,0.00001171498,0.001143174,0.00006463264,0.0007289395,0.002115289,0.00004202974,0.0001301162,0.00009417442],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9946134,"threshold_uncertainty_score":0.9998729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01121034614478025,"score_gpt":0.2036800403876355,"score_spread":0.1924696942428553,"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."}}