{"id":"W2069337468","doi":"10.1088/0964-1726/22/2/025027","title":"Compensation of rate-dependent hysteresis nonlinearities in a magnetostrictive actuator using an inverse Prandtl–Ishlinskii model","year":2013,"lang":"en","type":"article","venue":"Smart Materials and Structures","topic":"Piezoelectric Actuators and Control","field":"Engineering","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"U.S. Department of Energy","keywords":"Control theory (sociology); Hysteresis; Prandtl number; Feed forward; Actuator; Magnetostriction; Inverse; Compensation (psychology); Nonlinear system; Compensation methods; Materials science; Physics; Mechanics; Mathematics; Computer science; Engineering; Convection; Condensed matter physics; Control engineering; Magnetic field","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":[0.0002645562,0.0004549181,0.0003494198,0.0002410431,0.0002124058,0.000306298,0.0008433074,0.0004313506,0.0006464784],"category_scores_gemma":[0.0004394936,0.0002480349,0.0003858813,0.0001486779,0.0003663067,0.0005683982,0.0002795242,0.0004621001,0.0001576872],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003056903,"about_ca_system_score_gemma":0.0004816977,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001098743,"about_ca_topic_score_gemma":0.001620189,"domain_scores_codex":[0.999788,0.00002753557,0.00001283571,0.00005111064,0.0001043487,0.00001607207],"domain_scores_gemma":[0.9998612,0.00003465189,0.00004041129,0.00002431851,0.00003396643,0.000005434903],"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":[0.0001607245,0.0001551251,0.001401473,0.0003748935,0.00005268306,0.0003229122,0.0003266152,0.2498123,0.6804082,0.006857747,0.0004767641,0.05965051],"study_design_scores_gemma":[0.00001525123,0.0001892074,0.0007238238,0.0000108838,0.00001982387,0.000106741,0.00001831692,0.9357768,0.06107474,0.0006823431,0.001363876,0.00001822788],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1614461,0.0005926167,0.8306499,0.0002450947,0.0000846537,0.00009984837,0.00004268411,0.0008268336,0.006012289],"genre_scores_gemma":[0.9594209,0.0003111729,0.03776004,0.00003645546,0.00001165374,0.00007844121,0.00003302843,0.0000243204,0.002324026],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001098743,"threshold_uncertainty_score":0.002218008,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01192452479522517,"score_gpt":0.2062730427206969,"score_spread":0.1943485179254717,"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."}}