{"id":"W4391302569","doi":"10.2514/6.2024-0118","title":"Model Predictive Controller with Adaptive Neural Networks and Online State Estimation for Pitch Rate Control of the Cessna Citation X","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Artificial neural network; Model predictive control; Adaptive control; Computer science; Controller (irrigation); Control theory (sociology); Estimation; State (computer science); Control (management); Artificial intelligence; Engineering; Algorithm","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":[0.00034594,0.0005328097,0.0003210718,0.0002053675,0.0002572462,0.0004486298,0.0006210355,0.0004404958,0.0008893357],"category_scores_gemma":[0.0007146302,0.0001914297,0.0003309188,0.0002518842,0.0003082893,0.0003701614,0.0003396976,0.0007371661,0.0001787549],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000323484,"about_ca_system_score_gemma":0.0004150285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006507465,"about_ca_topic_score_gemma":0.004245168,"domain_scores_codex":[0.9997764,0.00003529165,0.00001225026,0.00005258557,0.0001049557,0.00001843343],"domain_scores_gemma":[0.9998507,0.00004741779,0.00003320679,0.00001361626,0.0000499802,0.000004985973],"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":[0.0001515731,0.00008840136,0.0008119827,0.0001784308,0.00006355202,0.0001469352,0.0001184574,0.7561875,0.03132311,0.005876973,0.001108227,0.2039449],"study_design_scores_gemma":[0.000006893193,0.0000552664,0.0002245696,0.000005420435,0.000008320315,0.000016713,0.000004497523,0.9960818,0.002469331,0.000307867,0.0008144191,0.000004937358],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03568012,0.0007895704,0.9562628,0.0001330655,0.0001111487,0.00004901063,0.00001417335,0.0005029967,0.006457165],"genre_scores_gemma":[0.9264185,0.0004289219,0.06805022,0.00006411558,0.00005959575,0.0001164827,0.00004451541,0.00002456253,0.004793045],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006507465,"threshold_uncertainty_score":0.01293916,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008459625994822612,"score_gpt":0.2111265422426416,"score_spread":0.202666916247819,"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."}}