{"id":"W2980804356","doi":"10.1007/s41315-019-00105-3","title":"Online model-free controller for flexible wing aircraft: a policy iteration-based reinforcement learning approach","year":2019,"lang":"en","type":"article","venue":"International Journal of Intelligent Robotics and Applications","topic":"Adaptive Dynamic Programming Control","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"Ontario Centres of Excellence","keywords":"Aerodynamics; Reinforcement learning; Control theory (sociology); Wing; Kinematics; Controller (irrigation); Stability (learning theory); Computer science; Optimal control; Control engineering; Flight dynamics; Nonlinear system; Engineering; Control (management); Artificial intelligence; Mathematical optimization; Aerospace engineering; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.0003250318,0.0001383437,0.0002087466,0.0003112329,0.00009374969,0.000237989,0.001015909,0.00004903297,0.000002211098],"category_scores_gemma":[0.0001158784,0.0001240588,0.0001492307,0.000148363,0.00003333373,0.0002606421,0.0001438932,0.000193846,0.000003204082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001612921,"about_ca_system_score_gemma":0.0002262247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007765744,"about_ca_topic_score_gemma":0.000001034701,"domain_scores_codex":[0.9986357,0.00001973206,0.0005600057,0.0002117298,0.0003958229,0.0001770227],"domain_scores_gemma":[0.9980859,0.0001899434,0.0004407985,0.0002231639,0.000964172,0.00009606594],"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":[0.00002443526,0.0001240376,0.00008199849,0.000007024999,0.00007997253,2.779127e-7,0.00008149845,0.7159318,0.0002963395,0.2685083,0.00002561143,0.01483868],"study_design_scores_gemma":[0.00106648,0.0001301304,0.00002465271,0.0000340375,0.00001751978,0.00001422195,0.00003841468,0.9831626,0.0001511128,0.01140806,0.003836015,0.0001167266],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0002499517,0.00009878005,0.9948555,0.003877183,0.0001029072,0.000541639,0.000008442974,0.00003319392,0.000232369],"genre_scores_gemma":[0.6702481,0.00003120262,0.3282704,0.0005647567,0.0002546243,0.00005439355,0.00002639665,0.00001149649,0.0005386446],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6699981,"threshold_uncertainty_score":0.5058971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01919894647478068,"score_gpt":0.2903157640076766,"score_spread":0.271116817532896,"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."}}