{"id":"W4385488776","doi":"10.3390/drones7080503","title":"Fault Detection and Fault-Tolerant Cooperative Control of Multi-UAVs under Actuator Faults, Sensor Faults, and Wind Disturbances","year":2023,"lang":"en","type":"article","venue":"Drones","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":10,"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; Government of Jiangsu Province; Nanjing University of Aeronautics and Astronautics; Chinese Aeronautical Establishment; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Control theory (sociology); Backstepping; Actuator; Fault detection and isolation; Fault (geology); Lyapunov function; Computer science; Engineering; Control engineering; Control (management); Adaptive control; Nonlinear system; Artificial intelligence","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":[0.0003593838,0.0003411426,0.0005735802,0.0001717752,0.0002613072,0.0001407742,0.0003634711,0.00015546,0.000003706017],"category_scores_gemma":[0.0001347753,0.000285671,0.0000773034,0.0004472848,0.00020729,0.0005737005,0.000146167,0.0001711402,0.00004366086],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004883818,"about_ca_system_score_gemma":0.00004465318,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002569749,"about_ca_topic_score_gemma":0.0002126662,"domain_scores_codex":[0.9977102,0.0002162049,0.0005145674,0.0007133741,0.0003968442,0.0004488305],"domain_scores_gemma":[0.9985166,0.0003718474,0.0002662028,0.0004396966,0.0002309359,0.0001747551],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001110142,0.001321528,0.02934984,0.001371239,0.002595168,0.0003693194,0.02702329,0.0249848,0.700691,0.01353984,0.001098797,0.196545],"study_design_scores_gemma":[0.008153523,0.0003264991,0.09479143,0.0001686905,0.00008607012,0.00006013282,0.002913738,0.8782332,0.01174013,0.0001473924,0.002610313,0.0007689049],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7328926,0.001088023,0.2631115,0.0007961768,0.0005983008,0.0008866136,0.0002532286,0.0003393587,0.00003414324],"genre_scores_gemma":[0.9987189,0.0000937434,0.0005621354,0.00009557691,0.00007991322,0.00005326439,0.00001961943,0.00002205711,0.0003547867],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8532484,"threshold_uncertainty_score":0.9999595,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01687773053502796,"score_gpt":0.2473243046915721,"score_spread":0.2304465741565441,"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."}}