{"id":"W2135219152","doi":"10.1109/tsmcb.2008.2006368","title":"Adaptive Neural Control for a Class of Uncertain Nonlinear Systems in Pure-Feedback Form With Hysteresis Input","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)","topic":"Piezoelectric Actuators and Control","field":"Engineering","cited_by":214,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Control theory (sociology); Nonlinear system; Tracking error; Hysteresis; Artificial neural network; Bounded function; Adaptive control; Lyapunov function; Function (biology); Class (philosophy); Mathematics; Backstepping; Computer science; Control (management); Mathematical analysis; Artificial intelligence; Physics","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":[0.0001882651,0.0004308501,0.0007548106,0.0002573046,0.000113101,0.00005793188,0.0002035845,0.0002547193,0.00000626845],"category_scores_gemma":[0.000004793026,0.0003949438,0.0001407778,0.0003048882,0.0001442741,0.0001065447,0.000001837569,0.00033133,0.000007827885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001283348,"about_ca_system_score_gemma":0.00006506773,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004691375,"about_ca_topic_score_gemma":0.0004127676,"domain_scores_codex":[0.9978092,0.00008166516,0.000767999,0.0003945522,0.0003751563,0.0005714154],"domain_scores_gemma":[0.9988297,0.0002818554,0.0001690521,0.0003688292,0.0001498701,0.0002007102],"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":[0.001701111,0.000613995,0.0008848078,0.001240988,0.00120999,0.00009839699,0.00267917,0.9664741,0.0007451387,0.001806297,0.0014978,0.02104826],"study_design_scores_gemma":[0.004417437,0.001129442,0.0001678035,0.0003206024,0.0001760785,0.0001259244,0.0004454166,0.9897047,0.0006084566,0.00001961115,0.002372348,0.0005122261],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5699757,0.002447007,0.4216318,0.00006598962,0.001022053,0.002649085,0.000456822,0.0002060868,0.001545463],"genre_scores_gemma":[0.9982034,0.000300219,0.000130712,0.00003352778,0.0001359986,0.0004711936,0.000006759399,0.00009406067,0.0006241367],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4282278,"threshold_uncertainty_score":0.9998503,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01443597883117742,"score_gpt":0.2007339821464547,"score_spread":0.1862980033152772,"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."}}