{"id":"W3199610458","doi":"10.1049/cth2.12169","title":"Second‐order adaptive integral terminal sliding mode approach to tracking control of robotic manipulators","year":2021,"lang":"en","type":"article","venue":"IET Control Theory and Applications","topic":"Adaptive Control of Nonlinear Systems","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"National Natural Science Foundation of China","keywords":"Control theory (sociology); Robot manipulator; Terminal sliding mode; Terminal (telecommunication); Tracking (education); Integral sliding mode; Computer science; Sliding mode control; Control engineering; Adaptive control; Control (management); Engineering; Artificial intelligence; Nonlinear system; 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":[],"consensus_categories":[],"category_scores_codex":[0.0003840492,0.0002432083,0.0005101284,0.00009897954,0.0001074431,0.00004399595,0.0001759881,0.0001070728,0.00002603621],"category_scores_gemma":[0.00006489242,0.0002410812,0.0001102399,0.0002495818,0.00005800486,0.0001387883,0.00002338396,0.0002287609,0.00001243675],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004802173,"about_ca_system_score_gemma":0.00003793828,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003319808,"about_ca_topic_score_gemma":0.00001039912,"domain_scores_codex":[0.998663,0.0001609531,0.0004256547,0.0003263836,0.0001356191,0.0002883897],"domain_scores_gemma":[0.9987877,0.0004366193,0.00008454145,0.0003284006,0.0002133982,0.0001493489],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002546529,0.0001928177,0.0002840559,0.0001692667,0.000639736,0.000009660571,0.0008273497,0.2261319,0.06262384,0.6948298,0.00006607061,0.01397078],"study_design_scores_gemma":[0.004856942,0.0001244179,0.0022972,0.0001557992,0.0004702426,0.0001509026,0.0029783,0.9686356,0.00353424,0.01126277,0.004638295,0.0008952757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0118693,0.001015481,0.9812618,0.00004390466,0.00007766934,0.0008260111,0.0001153124,0.0001238181,0.004666673],"genre_scores_gemma":[0.9958109,0.000005857277,0.003076656,0.000144843,0.0002419089,0.0004564193,0.0000141805,0.00004755242,0.000201738],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9839416,"threshold_uncertainty_score":0.9831005,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01538369536010229,"score_gpt":0.2401215261204544,"score_spread":0.2247378307603521,"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."}}