{"id":"W4388958778","doi":"10.1016/j.compchemeng.2023.108511","title":"A practically implementable reinforcement learning control approach by leveraging offset-free model predictive control","year":2023,"lang":"en","type":"article","venue":"Computers & Chemical Engineering","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"","keywords":"Offset (computer science); Reinforcement learning; Model predictive control; Control theory (sociology); Computer science; Nonlinear system; Control engineering; Process (computing); Controller (irrigation); Implementation; Process control; Optimal control; Control (management); Engineering; Mathematical optimization; Artificial intelligence; Mathematics","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":[0.000641983,0.0005655373,0.0006125987,0.0001956579,0.0003411542,0.0007314668,0.0009502786,0.0008567522,0.002668479],"category_scores_gemma":[0.001722637,0.0002480004,0.0002358465,0.0001950548,0.0006310786,0.0005381046,0.001068341,0.001365518,0.0005038148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003347362,"about_ca_system_score_gemma":0.0008385341,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002932363,"about_ca_topic_score_gemma":0.003183935,"domain_scores_codex":[0.9995428,0.0001128467,0.00001848037,0.0000777707,0.0002023895,0.00004569384],"domain_scores_gemma":[0.9995354,0.0002093,0.00003897972,0.00007816502,0.0001062899,0.00003190492],"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":[0.0002454359,0.0001858774,0.000464789,0.0001370762,0.00004170959,0.0001633162,0.00007687768,0.7706709,0.01717573,0.03610721,0.002263233,0.1724678],"study_design_scores_gemma":[0.00001321381,0.00003934583,0.00004073259,0.000003742268,0.000002999716,0.00001164361,0.000002172828,0.9959707,0.0008230443,0.002618247,0.000471065,0.000003081237],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01146321,0.0001396755,0.9810809,0.0001966402,0.00009689714,0.00004840287,0.00001701906,0.0007354107,0.006221703],"genre_scores_gemma":[0.8928589,0.00007984101,0.1035251,0.0001073341,0.00003546551,0.00008251287,0.00002725226,0.00004881486,0.003234932],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002932363,"threshold_uncertainty_score":0.008926928,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006049674006604016,"score_gpt":0.1929514060746059,"score_spread":0.1869017320680019,"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."}}