{"id":"W2765892911","doi":"10.1016/j.ifacol.2017.08.1368","title":"Control of an electromechanical clutch actuator by a parallel Adaptive Feedforward and Bang-Bang controller: Simulation and Experimental results","year":2017,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Iterative Learning Control Systems","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Feed forward; Clutch; Control theory (sociology); Bang–bang control; Actuator; Controller (irrigation); Adaptive control; Control (management); Control engineering; Computer science; Engineering; Optimal control; Mathematics; Automotive engineering; Biology","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":[0.0003249272,0.0003004925,0.0005997813,0.00006316039,0.0002195686,0.0001291605,0.0001689678,0.0001929205,0.00001004989],"category_scores_gemma":[0.0001896567,0.0002767183,0.00006440574,0.0000326299,0.0001026708,0.0003914577,0.00003122426,0.0002590203,0.00000401401],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007023772,"about_ca_system_score_gemma":0.00001422543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009650717,"about_ca_topic_score_gemma":0.00003681342,"domain_scores_codex":[0.9985113,0.0001209928,0.0004219297,0.000370631,0.0002335708,0.0003416255],"domain_scores_gemma":[0.9989857,0.0002576907,0.0002174592,0.0003003772,0.00007060175,0.0001681433],"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":[0.004012882,0.0001712037,0.0003353673,0.0000548586,0.0005401982,0.00001834801,0.002812598,0.02676077,0.9586649,0.0003423167,0.00002192899,0.006264609],"study_design_scores_gemma":[0.01199763,0.0008775516,0.001174152,0.00005024751,0.00005061604,0.000009275852,0.0002626975,0.9810541,0.003913015,0.00001559795,0.0002889425,0.0003061697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9826842,0.001418772,0.01409225,0.0002454707,0.000119177,0.0007647968,0.000315276,0.0001444054,0.0002156768],"genre_scores_gemma":[0.9933975,0.00001136577,0.00611659,0.00005495821,0.000193211,0.00003467012,0.00004719745,0.00004920411,0.00009535164],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9547519,"threshold_uncertainty_score":0.9999685,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01039697098785204,"score_gpt":0.2634571285328449,"score_spread":0.2530601575449928,"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."}}