{"id":"W2911341325","doi":"10.23919/tems.2018.8326461","title":"Fuzzy sliding mode control based on longitudinal force estimation for electro-mechanical braking systems using BLDC motor","year":2018,"lang":"en","type":"article","venue":"CES Transactions on Electrical Machines and Systems","topic":"Vehicle Dynamics and Control Systems","field":"Engineering","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"National Natural Science Foundation of China","keywords":"Control theory (sociology); Brake; Sliding mode control; Axle; Controller (irrigation); Slip ratio; Fuzzy logic; Computer science; Control engineering; Traction control system; Nonlinear system; Engineering; Automotive engineering; Control (management); Mechanical engineering","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":[0.0003534074,0.0003736198,0.0005861007,0.0003070499,0.0005054628,0.0002979429,0.0001542159,0.0002278099,0.000002226946],"category_scores_gemma":[0.00002320163,0.0003316246,0.0001826174,0.0003310103,0.00002413527,0.0001542951,0.000001461111,0.0002907699,0.000003441936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002548533,"about_ca_system_score_gemma":0.00003024812,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002482243,"about_ca_topic_score_gemma":0.00002186436,"domain_scores_codex":[0.9979466,0.0001099117,0.000589869,0.000442768,0.0003211548,0.0005897126],"domain_scores_gemma":[0.9989262,0.0004068249,0.0001155308,0.0002512395,0.0001268398,0.0001734171],"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":[0.0003510431,0.00006700506,0.0001215633,0.0002721362,0.0001403392,0.000002350959,0.00001762859,0.9714215,0.01532071,0.005010006,0.000008910727,0.007266815],"study_design_scores_gemma":[0.001558149,0.0008291056,0.00006599275,0.0002005725,0.0001143137,0.00004207633,0.000010958,0.9965151,0.0001056752,0.0001667382,0.00003697741,0.0003543478],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06289618,0.0004623391,0.9338438,0.00003251675,0.0009700253,0.001176869,0.00008530838,0.0002858476,0.0002471464],"genre_scores_gemma":[0.9988315,0.0000115926,0.0001819162,0.00003005683,0.0004608071,0.0002915231,0.000009084365,0.00008151174,0.0001019935],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9359353,"threshold_uncertainty_score":0.9999136,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01192611239232585,"score_gpt":0.2450620759248322,"score_spread":0.2331359635325063,"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."}}