{"id":"W1536662367","doi":"10.1109/acc.2015.7170729","title":"Multiple-model based adaptive compensation of actuation sign uncertainty using an error transformation","year":2015,"lang":"en","type":"article","venue":"","topic":"Adaptive Control of Nonlinear Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Control theory (sociology); Estimator; Computer science; Transformation (genetics); Controller (irrigation); Compensation (psychology); Sign (mathematics); Tracking error; Adaptive control; SIGNAL (programming language); Tracking (education); Stability (learning theory); Scheme (mathematics); Control engineering; Control (management); Mathematics; Artificial intelligence; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003223247,0.000131997,0.0001946868,0.0001116619,0.00002404676,0.00001214785,0.00007433796,0.00008044489,0.000007184053],"category_scores_gemma":[0.00004149183,0.0001267233,0.00004339765,0.0001066509,0.00002082809,0.0005612977,0.000003183343,0.00006706136,0.000006254375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002139137,"about_ca_system_score_gemma":0.00008169679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001516571,"about_ca_topic_score_gemma":0.0002443884,"domain_scores_codex":[0.9991392,0.00004411204,0.0003330089,0.0001041992,0.000249084,0.0001303634],"domain_scores_gemma":[0.9993443,0.00006414531,0.00007500382,0.00013823,0.0002902484,0.00008804492],"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":[0.00007395513,0.0000272602,0.00004009894,0.00001633412,0.00001499391,1.650766e-7,0.0009487594,0.9678925,0.02947566,0.0002306786,0.000009228105,0.001270357],"study_design_scores_gemma":[0.001325432,0.0000930044,0.0002107624,0.00002272851,0.00002063049,6.656414e-7,0.0008835405,0.9920726,0.005144174,0.00006903088,0.00001785124,0.0001396051],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2638793,0.000009453192,0.7346593,0.00001159158,0.00007302567,0.0003086907,0.00001909858,0.0001293022,0.0009103182],"genre_scores_gemma":[0.973178,2.113367e-7,0.02664914,0.00001742056,0.00004637525,0.000008621349,0.00007017596,0.00002342261,0.000006674662],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7092987,"threshold_uncertainty_score":0.5167627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1119710160252399,"score_gpt":0.2898772376898973,"score_spread":0.1779062216646574,"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."}}