{"id":"W4405056024","doi":"10.1109/tcyb.2024.3505259","title":"Observer-Based Adaptive Fixed-Time Sensor Fault Compensation Control for Uncertain Nonlinear Systems","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Cybernetics","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Science Foundation of Zhejiang Province; Natural Science Foundation of Liaoning Province; National Natural Science Foundation of China; National Science Foundation","keywords":"Control theory (sociology); Observer (physics); Nonlinear system; Compensation (psychology); Computer science; Adaptive control; Control (management); Control engineering; Engineering; Physics; Artificial intelligence; Psychology","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":[0.0004702467,0.0004923341,0.000503726,0.000225124,0.0002232665,0.0005350454,0.0007440743,0.0005559744,0.0007727182],"category_scores_gemma":[0.0008459335,0.0001529479,0.0004137111,0.0002723129,0.0004319806,0.0006397896,0.000438178,0.0005963782,0.0001081017],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000386668,"about_ca_system_score_gemma":0.0005536033,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005070397,"about_ca_topic_score_gemma":0.002990423,"domain_scores_codex":[0.9997111,0.00005048693,0.00002314314,0.00006289531,0.0001193027,0.00003305322],"domain_scores_gemma":[0.9997124,0.00008242754,0.00005988896,0.00002416993,0.0001110172,0.000009942161],"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":[0.0002277091,0.00005967651,0.0008360158,0.0003461724,0.00008058278,0.0002376564,0.0002323644,0.8223,0.03353144,0.02080306,0.001299888,0.1200455],"study_design_scores_gemma":[0.00001054942,0.00006583238,0.0001448563,0.000005005649,0.000007011046,0.00001911869,0.000005887913,0.9965473,0.001838983,0.0009017016,0.0004480734,0.000005797679],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01418509,0.0003244814,0.9839004,0.0000574297,0.00006912542,0.00001790784,0.00001204401,0.0001671036,0.001266255],"genre_scores_gemma":[0.9715578,0.0003425138,0.02621678,0.00003875565,0.00003531779,0.00005652536,0.0000524452,0.00001482962,0.001685072],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005070397,"threshold_uncertainty_score":0.01008177,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01842413414570308,"score_gpt":0.2337542997106759,"score_spread":0.2153301655649728,"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."}}