{"id":"W4200492172","doi":"10.1109/tnnls.2021.3131364","title":"Guaranteeing Global Stability for Neuro-Adaptive Control of Unknown Pure-Feedback Nonaffine Systems via Barrier Functions","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Adaptive Control of Nonlinear Systems","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"National Natural Science Foundation of China","keywords":"Backstepping; Control theory (sociology); Bounded function; Artificial neural network; Lyapunov stability; Adaptive control; Nonlinear system; Stability (learning theory); Lyapunov function; Computer science; Uniform boundedness; Adaptive system; Mathematics; Control (management); Artificial intelligence; Physics; Mathematical analysis","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":[0.0004732579,0.0004348117,0.0008767581,0.00008438392,0.0003855579,0.0001266017,0.0001352096,0.0002537493,0.00001350567],"category_scores_gemma":[0.00004140882,0.000427318,0.0002988659,0.0004010948,0.00009690391,0.0002117575,0.000002660755,0.0007178641,0.000004201624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000141946,"about_ca_system_score_gemma":0.00004149464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001210944,"about_ca_topic_score_gemma":0.0001074027,"domain_scores_codex":[0.9972656,0.0005159715,0.0008563495,0.0005307172,0.00029813,0.000533253],"domain_scores_gemma":[0.9978253,0.0009925105,0.0002010088,0.0003441533,0.0004390272,0.0001979892],"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":[0.0002412257,0.00005441337,0.0002206059,0.0002611995,0.0003236652,0.00001295585,0.00005941386,0.9929097,0.002101211,0.00007044442,0.00006175083,0.003683471],"study_design_scores_gemma":[0.001820376,0.0003966303,0.0001767074,0.0002109832,0.0001899715,0.0001225674,0.0005392249,0.9934608,0.00007592118,0.000001440134,0.002658715,0.0003466268],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04849674,0.004145074,0.9408972,0.00004074839,0.004684214,0.001073747,0.0002221174,0.0003140615,0.0001260715],"genre_scores_gemma":[0.9987022,0.00004259757,0.00006639247,0.00002212554,0.0005483936,0.0002099712,0.00001379697,0.00007941131,0.0003151504],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9502054,"threshold_uncertainty_score":0.9998178,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01240426538962236,"score_gpt":0.2080933148965348,"score_spread":0.1956890495069124,"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."}}