{"id":"W2921240485","doi":"10.1109/tnnls.2019.2899632","title":"3-D Learning-Enhanced Adaptive ILC for Iteration-Varying Formation Tasks","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Iterative Learning Control Systems","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Key Technology Research and Development Program of Shandong; National Natural Science Foundation of China","keywords":"Iterative learning control; Dimension (graph theory); Computer science; Learnability; Asynchronous communication; Convergence (economics); Linearization; Nonlinear system; Control theory (sociology); Iterative method; Feedback linearization; Algorithm; Mathematics; Control (management); Artificial intelligence","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":[0.0006359939,0.000577372,0.0005283558,0.0003349485,0.0003891798,0.0006297718,0.000893719,0.0006468247,0.00124503],"category_scores_gemma":[0.001187208,0.0002757354,0.0004231276,0.0003496599,0.0007033076,0.0004930994,0.001012576,0.0009256605,0.0002805927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007525679,"about_ca_system_score_gemma":0.0008835117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006816506,"about_ca_topic_score_gemma":0.004405613,"domain_scores_codex":[0.9997222,0.0000513497,0.00001552453,0.00006773294,0.00009761444,0.0000455819],"domain_scores_gemma":[0.9994886,0.0001902074,0.0001074779,0.0000491892,0.0001363887,0.00002808779],"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":[0.00005152488,0.00001850727,0.0003602408,0.00004027899,0.00001666164,0.00005141699,0.00009698561,0.9641258,0.002889175,0.004079339,0.0004413855,0.0278286],"study_design_scores_gemma":[0.00000354736,0.00001575418,0.00004234153,0.000001770588,0.000001869397,0.000006443963,0.000003291901,0.9987068,0.0004023469,0.0005421517,0.0002711317,0.000002573838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01390478,0.0001654034,0.9823599,0.00008082165,0.00002912775,0.00002823702,0.0000120349,0.0002372782,0.003182345],"genre_scores_gemma":[0.9326343,0.0001400707,0.06439821,0.0000990414,0.00002826553,0.0001131012,0.00004602182,0.00003677713,0.002504228],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006816506,"threshold_uncertainty_score":0.01355368,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008496982150428184,"score_gpt":0.2044319889112695,"score_spread":0.1959350067608414,"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."}}