{"id":"W2089701123","doi":"10.1002/cjce.21965","title":"Design of a robust internal model control PID controller based on linear quadratic gaussian tuning strategy","year":2014,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Advanced Control Systems Design","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Beijing University of Chemical Technology; Beijing University of Technology; National Natural Science Foundation of China","keywords":"PID controller; Control theory (sociology); Linear-quadratic-Gaussian control; Internal model; Robustness (evolution); Sensitivity (control systems); Optimal projection equations; Dead time; Transfer function; Computer science; Control engineering; Mathematics; Optimal control; Engineering; Temperature control; Mathematical optimization; Control (management)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006836973,0.0002582389,0.0005553188,0.0002296597,0.00003831102,0.00004091655,0.0004275141,0.0001189302,0.00001085483],"category_scores_gemma":[0.0003246525,0.0002029687,0.0001454441,0.0001332078,0.00005037633,0.0001078525,0.000003767173,0.000604565,0.000002924413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00027917,"about_ca_system_score_gemma":0.0002164778,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009685181,"about_ca_topic_score_gemma":0.00002381372,"domain_scores_codex":[0.9984793,0.00006386237,0.0006580344,0.0001069335,0.0002606928,0.0004311504],"domain_scores_gemma":[0.9985593,0.000484659,0.0001706255,0.0002174418,0.0001210526,0.0004469274],"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":[0.00005410299,0.00000381253,0.000004309039,0.00003639761,0.000075637,0.00001066947,0.00006549183,0.8893015,0.1097348,0.0003165693,0.00003777779,0.0003589145],"study_design_scores_gemma":[0.001783633,0.0001030518,0.000004256135,0.0002871696,0.0000510838,0.00002571083,0.000004974818,0.9877807,0.009602715,0.0001368686,0.00003348446,0.0001862766],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005720303,0.0002541585,0.993308,0.0001227011,0.0001578291,0.0001858751,0.000005494248,0.00003571561,0.0002099229],"genre_scores_gemma":[0.993989,7.332338e-7,0.005593361,0.00008412148,0.000247861,0.000008706641,5.389853e-7,0.00006698185,0.000008684045],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9882687,"threshold_uncertainty_score":0.8276822,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01453492987124013,"score_gpt":0.1864464046405908,"score_spread":0.1719114747693506,"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."}}