{"id":"W4312302359","doi":"10.1109/icarm54641.2022.9959352","title":"Multi-Objective Admittance Control: An LMI-Based Method","year":2022,"lang":"en","type":"article","venue":"2022 International Conference on Advanced Robotics and Mechatronics (ICARM)","topic":"Prosthetics and Rehabilitation Robotics","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"National Natural Science Foundation of China","keywords":"Admittance; Robustness (evolution); Control theory (sociology); Passivity; Robust control; Admittance parameters; Computer science; Robot; Control engineering; Control system; Engineering; Control (management); Electrical impedance; Voltage; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003269473,0.0002653792,0.0002746376,0.0001810002,0.000248195,0.00007003082,0.0003887647,0.0000689657,0.0002288065],"category_scores_gemma":[0.00005354168,0.0002823031,0.00009536311,0.0001540048,0.00005147535,0.0001288231,0.00009225038,0.0005744123,0.000006298056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002855449,"about_ca_system_score_gemma":0.0001128679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003449109,"about_ca_topic_score_gemma":0.00001392825,"domain_scores_codex":[0.9983232,0.0001121113,0.0003319379,0.0004370532,0.0004783127,0.0003174015],"domain_scores_gemma":[0.9990582,0.0001307598,0.0001043155,0.0003068314,0.0002372322,0.0001626777],"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":[0.00006163172,0.0001320712,0.00003667683,0.00001207541,0.00005398924,0.00000424497,0.0001184537,0.8212759,0.008603062,0.163903,0.00001939121,0.005779535],"study_design_scores_gemma":[0.001792871,0.0004462785,0.0001115229,0.0000174414,0.00002396453,0.000006075771,0.0005467814,0.9871123,0.0007031402,0.005494063,0.003407127,0.0003384749],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005187622,0.0004688693,0.9877638,0.001744813,0.002145773,0.0005645655,0.0004468482,0.0002582993,0.001419408],"genre_scores_gemma":[0.8661771,0.0002028189,0.1325342,0.0004651553,0.00004567812,0.0001483426,0.0001143311,0.00005734888,0.0002550117],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8609895,"threshold_uncertainty_score":0.9999629,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01958398972552013,"score_gpt":0.289882857674142,"score_spread":0.2702988679486218,"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."}}