{"id":"W1982458123","doi":"10.1016/j.asoc.2015.04.021","title":"A probabilistic approach for designing nonlinear optimal robust tracking controllers for unmanned aerial vehicles","year":2015,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Adaptive Control of Nonlinear Systems","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Control theory (sociology); Robustness (evolution); Parametric statistics; Computer science; Nonlinear system; Probabilistic logic; Monte Carlo method; Controller (irrigation); Mathematical optimization; Optimal control; Overshoot (microwave communication); Control engineering; Engineering; Mathematics; Control (management); Artificial intelligence","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001170259,0.0004028354,0.0007096365,0.0001109065,0.0002249131,0.0001549001,0.0003207077,0.0001936989,6.306141e-7],"category_scores_gemma":[0.0003683237,0.0004244323,0.0001821435,0.0001588659,0.00006509275,0.0001074315,0.00005495902,0.0002141691,0.000005321638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002085696,"about_ca_system_score_gemma":0.00009955893,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004010138,"about_ca_topic_score_gemma":0.000001794636,"domain_scores_codex":[0.9978005,0.00003691287,0.0006609621,0.0004953584,0.0002487185,0.0007575121],"domain_scores_gemma":[0.9982399,0.0008712847,0.0001726581,0.000239161,0.0002652048,0.0002117899],"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":[0.0003941483,0.00004684826,0.00001502474,0.000216017,0.0001506078,0.000001208083,0.0006769847,0.9882063,0.003889044,0.0005470368,0.0004348505,0.005421919],"study_design_scores_gemma":[0.006375111,0.0001222266,0.000009170906,0.00004532498,0.00008396852,0.000006563116,0.0008368531,0.9903325,0.0005819352,0.0001093587,0.001029922,0.0004670853],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03312133,0.0001830516,0.961748,0.00001973348,0.0004860671,0.003068748,0.00003106158,0.0007525737,0.0005894225],"genre_scores_gemma":[0.5890045,2.009291e-7,0.4093182,0.00002787278,0.001244353,0.0002251359,0.00005887785,0.0001102157,0.00001069203],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5558832,"threshold_uncertainty_score":0.9998208,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05212646527569322,"score_gpt":0.2442803119223405,"score_spread":0.1921538466466473,"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."}}