{"id":"W2549141920","doi":"10.12700/aph.12.1.2015.1.9","title":"Constrained Data-Driven Model-Free ILC-based Reference Input Tuning Algorithm","year":2014,"lang":"en","type":"article","venue":"Acta Polytechnica Hungarica","topic":"Iterative Learning Control Systems","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"European Social Fund; Natural Sciences and Engineering Research Council of Canada; Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii; Autoritatea Natională pentru Cercetare Stiintifică","keywords":"Iterative learning control; Computer science; Control theory (sociology); Curse of dimensionality; Trajectory; Iterative method; Algorithm; Process (computing); Artificial intelligence; Control (management)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005643621,0.00041307,0.000542429,0.000227466,0.0001856645,0.0001491625,0.001986618,0.000292492,0.00004633269],"category_scores_gemma":[0.0004661505,0.0004209485,0.00007655683,0.0003205777,0.0001471073,0.0003395003,0.0003874089,0.0008286665,0.00006967116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001258996,"about_ca_system_score_gemma":0.0001023436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004557139,"about_ca_topic_score_gemma":0.0000279761,"domain_scores_codex":[0.9976092,0.0001688342,0.000542188,0.0006119061,0.0004215698,0.0006462939],"domain_scores_gemma":[0.9964898,0.0003912837,0.0001449953,0.002685512,0.00009606336,0.0001923709],"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":[0.00006236757,0.0001795221,0.0002281271,0.0002548327,0.0004392058,0.00003986147,0.0007743294,0.6377677,0.1759461,0.005131814,0.04678256,0.1323937],"study_design_scores_gemma":[0.0008525047,0.00008001908,0.00008034232,0.00008677128,0.00004415249,0.00001028974,0.00001419326,0.9601046,0.0003525513,0.0001232688,0.0377852,0.0004660806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001077868,0.0001191496,0.9720118,0.0007096372,0.0002221047,0.0004283342,0.0002136688,0.002356961,0.02286049],"genre_scores_gemma":[0.9061212,0.000007059798,0.09286629,0.0002721282,0.0001700049,0.00005346369,0.0002258459,0.0001016753,0.0001823397],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9050433,"threshold_uncertainty_score":0.9998242,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0238398618544175,"score_gpt":0.2414506537617693,"score_spread":0.2176107919073518,"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."}}