{"id":"W3201876542","doi":"10.1109/tmech.2021.3112470","title":"Discrete-Time Adaptive Neural Tracking Control and Its Experiments for Quadrotor Unmanned Aerial Vehicle Systems","year":2021,"lang":"en","type":"article","venue":"IEEE/ASME Transactions on Mechatronics","topic":"Adaptive Control of Nonlinear Systems","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Japan Society for the Promotion of Science; National Natural Science Foundation of China","keywords":"Backstepping; Underactuation; Control theory (sociology); Adaptive control; Computer science; Control engineering; Artificial neural network; Nonlinear system; Scheme (mathematics); Tracking (education); Control (management); Engineering; Artificial intelligence; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002056493,0.0004094422,0.0006072629,0.0001143047,0.0002477081,0.0001356044,0.0001646119,0.0002273422,0.00003232716],"category_scores_gemma":[0.00001706414,0.0004386015,0.0002356996,0.0001538301,0.00002977136,0.0003790096,0.000003041005,0.0003295736,0.0000479239],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002728422,"about_ca_system_score_gemma":0.00007370722,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009934996,"about_ca_topic_score_gemma":0.00001867499,"domain_scores_codex":[0.9979724,0.0001430078,0.000516699,0.000472149,0.0003098188,0.0005858757],"domain_scores_gemma":[0.9989379,0.0002556549,0.00009100831,0.0003086456,0.0002012084,0.0002055977],"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":[0.0009880606,0.0002059798,0.000002980247,0.0002239888,0.0009848461,0.00003464553,0.0005479275,0.5529588,0.4402846,0.001438175,0.0001663869,0.002163629],"study_design_scores_gemma":[0.004477047,0.0003769278,0.000008209198,0.00009975045,0.0001517059,0.00002498401,0.0004530356,0.9563147,0.03639793,0.00002916561,0.001218059,0.0004485157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1010916,0.004080652,0.8869707,0.0001863112,0.003612143,0.002373792,0.0009981137,0.0005848085,0.0001018331],"genre_scores_gemma":[0.997761,0.00003634677,0.0006598046,0.00003516204,0.0004384163,0.0005697702,0.00001859578,0.000136396,0.0003445029],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8966694,"threshold_uncertainty_score":0.9998066,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0214552417297396,"score_gpt":0.2484532496866675,"score_spread":0.2269980079569279,"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."}}