{"id":"W2951792637","doi":"10.48550/arxiv.1807.05290","title":"Adaptive Model Predictive Control for High-Accuracy Trajectory Tracking in Changing Conditions","year":2018,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dynamic Systems Analysis (Canada); University of Toronto","funders":"Consejo Nacional de Ciencia y Tecnología","keywords":"Control theory (sociology); Trajectory; Model predictive control; Controller (irrigation); Computer science; Parametric statistics; Adaptive control; Tracking (education); Tracking error; Control engineering; Control (management); Artificial intelligence; Engineering; Mathematics","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":[0.0003734199,0.0005924437,0.0004039437,0.0002573353,0.0003174737,0.0004961549,0.0006492169,0.0005107109,0.001402795],"category_scores_gemma":[0.001041861,0.0002484332,0.000264409,0.0003840908,0.0004458238,0.0004265324,0.000477195,0.0007629141,0.0002196972],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004128882,"about_ca_system_score_gemma":0.0006215516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007403386,"about_ca_topic_score_gemma":0.005761527,"domain_scores_codex":[0.9998109,0.00003995646,0.000006926526,0.0000362026,0.00008481343,0.00002116526],"domain_scores_gemma":[0.9997737,0.0001051957,0.0000356834,0.0000259817,0.00005230728,0.000007154465],"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":[0.00004077405,0.00002645045,0.000219245,0.00007090858,0.00001801809,0.0000494324,0.00002742504,0.9465635,0.003478933,0.006187624,0.0009983752,0.04231925],"study_design_scores_gemma":[0.000003165157,0.00001268909,0.00006907147,0.000002230979,0.000002087218,0.00000514447,0.000001870637,0.9980082,0.0003958468,0.001101214,0.0003968982,0.000001602786],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01417234,0.0004636726,0.981135,0.0001246355,0.00005508719,0.00002632727,0.00002717701,0.0003714636,0.003624341],"genre_scores_gemma":[0.9528646,0.0003462764,0.04372652,0.00006060647,0.00004304689,0.00009214505,0.00008111193,0.00003323305,0.002752406],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007403386,"threshold_uncertainty_score":0.01472056,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02971470377689466,"score_gpt":0.2636606236195398,"score_spread":0.2339459198426452,"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."}}