{"id":"W4394926811","doi":"10.1002/cjce.25261","title":"An integrated tube robust iterative learning model predictive control strategy based on dynamic partial least squares algorithm for batch processes","year":2024,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Iterative Learning Control Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Iterative learning control; Robustness (evolution); Control theory (sociology); Model predictive control; Partial least squares regression; Computer science; Trajectory; Tracking error; Variable (mathematics); Mathematical optimization; Ellipsoid; Algorithm; Mathematics; Control (management); Artificial intelligence; Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006731325,0.0007956025,0.0008830504,0.0003406206,0.0004895462,0.0009139843,0.001280051,0.0007848701,0.001402453],"category_scores_gemma":[0.00106224,0.00039391,0.0006048636,0.0004543746,0.0006559319,0.0008739629,0.000808844,0.0008943498,0.0002990105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005454838,"about_ca_system_score_gemma":0.001158347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0069682,"about_ca_topic_score_gemma":0.003153273,"domain_scores_codex":[0.9994661,0.0001142794,0.00002837781,0.0001369379,0.0002106782,0.00004361362],"domain_scores_gemma":[0.9996159,0.0001414716,0.00006020746,0.00003384468,0.0001341296,0.0000144303],"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":[0.0001109021,0.00004650853,0.0003740761,0.0001159348,0.00003908475,0.00007722693,0.00008343058,0.9064929,0.007392266,0.006354378,0.0009081633,0.07800508],"study_design_scores_gemma":[0.000004950122,0.00003538471,0.00004115072,0.000002046077,0.000002976702,0.000006728552,0.000002096991,0.9985806,0.0007193448,0.0003945927,0.0002064471,0.000003693506],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009988866,0.0001427227,0.9875158,0.00006666246,0.00002482624,0.00003195676,0.00001409363,0.0003124816,0.001902489],"genre_scores_gemma":[0.8463367,0.000212475,0.1491375,0.00008525352,0.00003650463,0.0002401247,0.0001126995,0.00006295548,0.00377566],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0069682,"threshold_uncertainty_score":0.01385528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006668591918850951,"score_gpt":0.2059111743226086,"score_spread":0.1992425824037576,"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."}}