{"id":"W4393065876","doi":"10.1109/tcsi.2024.3376608","title":"Neural Network Based Iterative Learning Control for Dynamic Hysteresis and Uncertainties in Magnetic Shape Memory Alloy Actuator","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems I Regular Papers","topic":"Shape Memory Alloy Transformations","field":"Materials Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"National Natural Science Foundation of China","keywords":"Hysteresis; Shape-memory alloy; Actuator; Artificial neural network; Alloy; Computer science; Control (management); Iterative learning control; Control theory (sociology); Materials science; Artificial intelligence; Physics; Condensed matter physics; Composite material","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":[0.0005228387,0.0005911058,0.0004423361,0.000216756,0.000400873,0.0005145464,0.0007898366,0.0006011838,0.0008493739],"category_scores_gemma":[0.0009865288,0.000234604,0.0003733165,0.0002511777,0.0004953594,0.0004944147,0.0005321098,0.0006390321,0.00009737765],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005615531,"about_ca_system_score_gemma":0.0006578527,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007593682,"about_ca_topic_score_gemma":0.006341649,"domain_scores_codex":[0.9998099,0.00003495179,0.00001585643,0.00005162413,0.00005994377,0.00002769306],"domain_scores_gemma":[0.99971,0.0001248362,0.00006010842,0.00001555723,0.00007939505,0.00001013726],"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":[0.000102936,0.00004958197,0.0005688124,0.000130907,0.00004573162,0.00008826181,0.0001298458,0.9112376,0.01237491,0.003982882,0.0004912179,0.07079738],"study_design_scores_gemma":[0.000002921102,0.00002375789,0.00006103677,0.000002348878,0.000003477679,0.00000585661,0.000002609599,0.9988065,0.0007479939,0.0002279513,0.0001130479,0.000002532868],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07697164,0.0008695003,0.9163708,0.0002778528,0.0001077759,0.00005264633,0.00002821865,0.0004077626,0.00491382],"genre_scores_gemma":[0.9767932,0.0001947311,0.02087989,0.00006224502,0.00002323275,0.00007472181,0.00002501459,0.00001377669,0.001933228],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007593682,"threshold_uncertainty_score":0.01509899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01251069998543875,"score_gpt":0.230565204967955,"score_spread":0.2180545049825163,"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."}}