{"id":"W4389161378","doi":"10.1109/tase.2023.3336933","title":"Sliding Mode Iterative Learning Control With Iteration-Dependent Parameter Learning Mechanism for Nonlinear Systems and Its Application","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Automation Science and Engineering","topic":"Iterative Learning Control Systems","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"National Natural Science Foundation of China","keywords":"Iterative learning control; Control theory (sociology); Nonlinear system; Convergence (economics); Computer science; Sliding mode control; Tracking error; Control engineering; Algorithm; Artificial intelligence; Engineering; Control (management); Physics","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":[0.0007122101,0.0006236311,0.0005411869,0.0002888889,0.0003242865,0.0006875513,0.0007828108,0.0009156635,0.001620202],"category_scores_gemma":[0.001497353,0.0002114256,0.0004890332,0.0003779397,0.0006598114,0.0006046846,0.0006950393,0.0008734966,0.0002541351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005231368,"about_ca_system_score_gemma":0.0007635567,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002915018,"about_ca_topic_score_gemma":0.001189997,"domain_scores_codex":[0.9996113,0.00008240632,0.00002859933,0.00009855966,0.000145362,0.000033824],"domain_scores_gemma":[0.9995653,0.000187703,0.00005088034,0.00003578582,0.0001453024,0.0000151171],"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":[0.0001119528,0.00007373816,0.0008528181,0.0003768482,0.00007531611,0.0001783386,0.0004152076,0.7739761,0.01426175,0.04926462,0.00148555,0.1589278],"study_design_scores_gemma":[0.000007682356,0.00005404031,0.00007757005,0.000007662381,0.000005294024,0.00002040729,0.000007851448,0.995235,0.001035586,0.002539229,0.001002146,0.000007561991],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01028558,0.00102488,0.9843435,0.0001423747,0.00005953582,0.00003550015,0.000008728415,0.0001567715,0.003943104],"genre_scores_gemma":[0.888245,0.001488006,0.1037965,0.0001221944,0.00009239509,0.0002394481,0.00005819588,0.00003591573,0.005922372],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002915018,"threshold_uncertainty_score":0.005796134,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009790262534760043,"score_gpt":0.2339471652813155,"score_spread":0.2241569027465555,"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."}}