{"id":"W2768918552","doi":"10.1002/cjce.23080","title":"Hybrid iterative learning fault‐tolerant guaranteed cost control design for multi‐phase batch processes","year":2017,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Iterative Learning Control Systems","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Control theory (sociology); Iterative learning control; Controller (irrigation); Actuator; Computer science; Convergence (economics); Dwell time; Mathematical optimization; Fault tolerance; Convex optimization; Mathematics; Regular polygon; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001089226,0.000838104,0.0008840553,0.0003396837,0.0004566096,0.001050188,0.001446795,0.0008785265,0.001181487],"category_scores_gemma":[0.001218316,0.0003987892,0.0005830236,0.000328551,0.0007054217,0.0005009155,0.0008114927,0.0007261719,0.000191415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001013532,"about_ca_system_score_gemma":0.001044754,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005416792,"about_ca_topic_score_gemma":0.002603242,"domain_scores_codex":[0.9991919,0.0001482167,0.00004473557,0.0002128917,0.0003123885,0.00008970605],"domain_scores_gemma":[0.9993671,0.0001936163,0.0001681036,0.00004733487,0.0002006037,0.00002333358],"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":[0.0001574477,0.00005922176,0.0003580307,0.0001417307,0.00005703644,0.0000845455,0.0001139465,0.9435369,0.01393023,0.00643088,0.0003938952,0.03473608],"study_design_scores_gemma":[0.00001186745,0.00006471523,0.00007112208,0.000002652638,0.000004742858,0.000005978725,0.000002751025,0.9982778,0.001016977,0.0003502643,0.0001875557,0.000003601836],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0272518,0.0002024431,0.9695874,0.00009834463,0.0000398157,0.00005217846,0.00001997112,0.0002222561,0.002525671],"genre_scores_gemma":[0.9625212,0.0000822649,0.03580232,0.00005061923,0.00001856726,0.000155211,0.00003412848,0.00001499696,0.001320688],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005416792,"threshold_uncertainty_score":0.0107705,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02204643354883283,"score_gpt":0.2492624551716582,"score_spread":0.2272160216228253,"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."}}