{"id":"W4392867058","doi":"10.48550/arxiv.2403.08987","title":"A Constrained Tracking Controller for Ramp and Sinusoidal Reference Signals using Robust Positive Invariance","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adaptive Control of Nonlinear Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Control theory (sociology); Tracking (education); Controller (irrigation); Computer science; Mathematics; Artificial intelligence; Control (management); Psychology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002877112,0.0004951165,0.0007372464,0.0002452764,0.0001097806,0.0001713025,0.0003008017,0.0004582604,0.00001390093],"category_scores_gemma":[0.00007753856,0.0005793105,0.0002041042,0.0002001703,0.0001662877,0.0001645918,0.0003085972,0.0007488986,0.00001413716],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003328986,"about_ca_system_score_gemma":0.0001639239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006513973,"about_ca_topic_score_gemma":0.00005360192,"domain_scores_codex":[0.9982373,0.00009173167,0.000337729,0.0008278157,0.00007732685,0.0004281582],"domain_scores_gemma":[0.9987611,0.0003601059,0.0001566993,0.0002873838,0.0002689926,0.0001656851],"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":[0.0002000168,0.0000205335,0.0000664672,0.0005402938,0.0006932069,0.0002581096,0.0002212188,0.9782373,0.006553682,0.01276633,0.00007043023,0.0003723943],"study_design_scores_gemma":[0.001500396,0.00004987386,0.00008065654,0.000933215,0.0003541879,0.000020361,0.0002303899,0.9908421,0.0001893988,0.005117016,0.00009400651,0.000588408],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2479975,0.001138408,0.7458959,0.00004578868,0.000540562,0.00152666,0.0007606352,0.0004419742,0.001652658],"genre_scores_gemma":[0.9971434,0.00003859075,0.002021497,0.00003301369,0.0002718935,0.000005155567,0.00003258842,0.00008989929,0.0003639541],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7491459,"threshold_uncertainty_score":0.9996659,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1315353286691291,"score_gpt":0.2074190063112652,"score_spread":0.07588367764213608,"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."}}