{"id":"W4399126336","doi":"10.1109/tcyb.2024.3398717","title":"Data-Driven Robust Finite-Iteration Learning Control for MIMO Nonrepetitive Uncertain Systems","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Cybernetics","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China; National Science Foundation","keywords":"MIMO; Iterative learning control; Control (management); Computer science; Control theory (sociology); Mathematics; Artificial intelligence; Channel (broadcasting); Telecommunications","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":[0.001518747,0.0009699423,0.0008723904,0.0003317701,0.000426255,0.001201228,0.001594175,0.000847655,0.0009871089],"category_scores_gemma":[0.003076797,0.0003674855,0.000606523,0.0004014952,0.001227332,0.0008202916,0.001164208,0.00146681,0.000230773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009242134,"about_ca_system_score_gemma":0.00117414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00479659,"about_ca_topic_score_gemma":0.003125735,"domain_scores_codex":[0.9988939,0.0002008747,0.00006930216,0.0003080523,0.0004165071,0.0001114033],"domain_scores_gemma":[0.9983206,0.000812466,0.0002949551,0.0001312518,0.0003994996,0.00004115724],"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":[0.00007917199,0.00003161407,0.0002862577,0.0001685813,0.00003410462,0.00007625305,0.000147282,0.9488345,0.004696074,0.0102712,0.0003575888,0.03501733],"study_design_scores_gemma":[0.000004964854,0.00004436286,0.00006279993,0.00000539027,0.000003889867,0.00001054446,0.000004221283,0.9973909,0.001202024,0.001000905,0.0002635057,0.000006538612],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01200421,0.0002280765,0.9859045,0.00006811597,0.00002731154,0.00002529974,0.00001531898,0.0001463194,0.001580936],"genre_scores_gemma":[0.9442737,0.0002352717,0.05336915,0.00008211873,0.00003422682,0.0001393436,0.00008095298,0.00003399893,0.001751177],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00479659,"threshold_uncertainty_score":0.009537339,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02694009614101715,"score_gpt":0.2536041798501867,"score_spread":0.2266640837091696,"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."}}