{"id":"W3091780093","doi":"10.1007/s00521-020-05391-8","title":"Adaptive composite neural network disturbance observer-based dynamic surface control for electrically driven robotic manipulators","year":2020,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Adaptive Control of Nonlinear Systems","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of New Brunswick","funders":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Backstepping; Control theory (sociology); Artificial neural network; Computer science; Lyapunov stability; Adaptive control; Observer (physics); A priori and a posteriori; Bounded function; Stability (learning theory); Control engineering; Mathematics; Control (management); Engineering; Artificial intelligence","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":[0.0002530669,0.0002817111,0.0003495557,0.0001393485,0.0001743072,0.0003457489,0.00049804,0.000396393,0.001072279],"category_scores_gemma":[0.0005369032,0.000164894,0.0001670081,0.0001371821,0.0002876916,0.0003559793,0.00038696,0.0003954003,0.0001840701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000244588,"about_ca_system_score_gemma":0.0002835174,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002269183,"about_ca_topic_score_gemma":0.003036638,"domain_scores_codex":[0.9998842,0.00001965913,0.000006242306,0.00002423844,0.00005187568,0.00001384605],"domain_scores_gemma":[0.9998198,0.00004991535,0.0000341847,0.00001305637,0.0000762266,0.000006767257],"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":[0.0003606761,0.0000956857,0.000584763,0.000194672,0.00003986249,0.0001606982,0.0001426981,0.7756984,0.06987125,0.006275336,0.001659415,0.1449165],"study_design_scores_gemma":[0.000009176464,0.00006571587,0.0002283985,0.000003008742,0.00000291525,0.000009019837,0.00000411879,0.9969081,0.002024514,0.0003350324,0.0004063981,0.000003552309],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0477598,0.0002128508,0.9476616,0.0001053397,0.0001115109,0.00002453072,0.00001674449,0.000271705,0.003835833],"genre_scores_gemma":[0.966845,0.0001238632,0.02824783,0.00004002134,0.00002982143,0.00005586135,0.00003624886,0.00001862104,0.004602768],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002269183,"threshold_uncertainty_score":0.004512012,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01902559462469043,"score_gpt":0.2343130401081516,"score_spread":0.2152874454834612,"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."}}