{"id":"W4408323957","doi":"10.1002/cjce.25666","title":"Robust optimal iterative learning control for constrained batch processes with nonuniform batch lengths","year":2025,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Iterative Learning Control Systems","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Iterative learning control; Robustness (evolution); Mathematical optimization; Control theory (sociology); Computer science; Batch processing; Regular polygon; Linear matrix inequality; Optimization problem; Iterative method; Convex optimization; Ellipsoid; Robust control; Control (management); Mathematics; Control system; Engineering; Artificial intelligence","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":[0.0003811457,0.0002562305,0.000424974,0.0002122987,0.0001376007,0.0001911248,0.0003020907,0.0001103858,0.00001413511],"category_scores_gemma":[0.0007757388,0.0001892722,0.00009251366,0.00032946,0.00008185349,0.0001930661,0.000005663019,0.000734164,0.000001373207],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003212515,"about_ca_system_score_gemma":0.0007281438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008290407,"about_ca_topic_score_gemma":0.0002399999,"domain_scores_codex":[0.9988145,0.00002330901,0.0004201721,0.0001209637,0.0001505116,0.0004705558],"domain_scores_gemma":[0.9983912,0.000698436,0.0001002611,0.0001124243,0.0004543803,0.0002433306],"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":[0.00005114394,0.000003102467,0.0001960919,0.0002843145,0.0003204115,0.0000212187,0.0009011224,0.9846883,0.01247274,0.0005472994,0.0002030351,0.0003111587],"study_design_scores_gemma":[0.004090008,0.0002303412,0.00007074899,0.001522656,0.0002009589,0.0002708427,0.0003136353,0.9535319,0.02947793,0.00003447363,0.009704809,0.0005516846],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3514583,0.002059289,0.640748,0.001849168,0.000741818,0.0009771225,0.00004450684,0.0001970247,0.001924742],"genre_scores_gemma":[0.9974267,0.000001775291,0.002045284,0.00005375093,0.0002609618,0.00003298914,0.000004152317,0.0000473884,0.000126932],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6459684,"threshold_uncertainty_score":0.7718296,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005199843086601885,"score_gpt":0.1794457357907615,"score_spread":0.1742458927041597,"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."}}