{"id":"W2911352365","doi":"10.1109/tnnls.2019.2893643","title":"Adaptive Discrete-Time Flight Control Using Disturbance Observer and Neural Networks","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Adaptive Control of Nonlinear Systems","field":"Engineering","cited_by":87,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Differentiator; Control theory (sociology); Backstepping; Discrete time and continuous time; Artificial neural network; Nonlinear system; Bounded function; Computer science; Lyapunov function; Observer (physics); Adaptive control; Mathematics; Control (management); Artificial intelligence; Filter (signal processing)","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":[0.0002776743,0.0004747942,0.0007166426,0.00009613956,0.000333342,0.000206961,0.0001345227,0.0002459729,0.00001641857],"category_scores_gemma":[0.000004178452,0.0004250841,0.0001427128,0.0002107264,0.00008360802,0.0003633557,0.000003303615,0.001134955,0.000009485931],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008028629,"about_ca_system_score_gemma":0.000006262607,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001045763,"about_ca_topic_score_gemma":0.00001245757,"domain_scores_codex":[0.99783,0.0003699776,0.0004908495,0.0004981037,0.00023052,0.0005805861],"domain_scores_gemma":[0.9988845,0.000441282,0.0001403861,0.0002571421,0.00006510474,0.0002115822],"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":[0.0001317574,0.00001137396,0.0008765152,0.00004954974,0.000157429,0.00001328412,0.00006890526,0.996402,0.0002834584,0.000008461305,0.00002630472,0.001970915],"study_design_scores_gemma":[0.001255399,0.0002662466,0.0009806954,0.0002047632,0.00009597155,0.0000811112,0.0001131251,0.9961863,0.000003546423,3.467431e-7,0.0003549174,0.0004576254],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.277884,0.004803087,0.7135355,0.0000287611,0.002448832,0.0008330347,0.00001715967,0.0003660225,0.00008356467],"genre_scores_gemma":[0.9982631,0.00006435175,0.00004522996,0.00004150798,0.0005675702,0.00003770913,0.000004569494,0.0001136399,0.0008623417],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7203791,"threshold_uncertainty_score":0.9998201,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009827264065323324,"score_gpt":0.1987433817155028,"score_spread":0.1889161176501795,"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."}}