{"id":"W4321084955","doi":"10.1049/csy2.12077","title":"Impedance learning adaptive super‐twisting control of a robotic exoskeleton for physical human‐robot interaction","year":2023,"lang":"en","type":"article","venue":"IET Cyber-Systems and Robotics","topic":"Prosthetics and Rehabilitation Robotics","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; College Ahuntsic","funders":"","keywords":"Exoskeleton; Impedance control; Control theory (sociology); Controller (irrigation); Adaptive control; Computer science; Actuator; Powered exoskeleton; Artificial neural network; Robot; Trajectory; Terminal sliding mode; Control engineering; Electrical impedance; Engineering; Sliding mode control; Simulation; Artificial intelligence; Control (management); Nonlinear system","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":[0.0002718333,0.0002786567,0.0001915147,0.0001491139,0.0001452882,0.0002221862,0.0002807324,0.000173743,0.00122862],"category_scores_gemma":[0.0003638193,0.0000909426,0.0001563102,0.00007687381,0.0002515326,0.0002020961,0.0003409238,0.0001538737,0.000146569],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008518583,"about_ca_system_score_gemma":0.0001501213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005738622,"about_ca_topic_score_gemma":0.0005316117,"domain_scores_codex":[0.9998971,0.00002689452,0.000007727588,0.00002007609,0.0000352879,0.00001284764],"domain_scores_gemma":[0.9998857,0.00003241905,0.00002970106,0.00001492967,0.00002724532,0.000009979624],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000762524,0.0002276095,0.001386959,0.0003705948,0.00005853099,0.0006298018,0.0003254506,0.3130131,0.4563676,0.004987339,0.001039518,0.220831],"study_design_scores_gemma":[0.0000408417,0.0005621666,0.001691465,0.00001368258,0.0000165681,0.000116379,0.00002998215,0.9788842,0.01597848,0.0009401325,0.001713668,0.00001251367],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1576588,0.0001450522,0.8372439,0.0001056475,0.00006371042,0.00008636551,0.00001685986,0.0004651263,0.00421465],"genre_scores_gemma":[0.9858086,0.00004210787,0.01279695,0.00001462964,0.000007031478,0.00003364311,0.000006911106,0.00000513177,0.001284935],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00122862,"threshold_uncertainty_score":0.004110157,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0192915263104679,"score_gpt":0.262455379267692,"score_spread":0.2431638529572241,"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."}}