{"id":"W2586771499","doi":"10.1115/imece2016-65090","title":"Model-Reference Based Adaptive Control for Enhancing Lateral Stability of Car-Trailer Systems","year":2016,"lang":"en","type":"article","venue":"","topic":"Vehicle Dynamics and Control Systems","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Trailer; Control theory (sociology); Controller (irrigation); Yaw; Vehicle dynamics; Stability (learning theory); Lyapunov stability; Adaptive control; Lyapunov function; Reference model; Computer science; Control system; Engineering; Control engineering; Automotive engineering; Control (management); Nonlinear system","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":[],"consensus_categories":[],"category_scores_codex":[0.0002715355,0.0001453364,0.0003374936,0.00003611287,0.00001896027,0.00001489852,0.0001151327,0.00008770997,0.00002243266],"category_scores_gemma":[0.00001699509,0.00009653607,0.00007637135,0.00004411547,0.00002111817,0.00009382433,0.000006244119,0.00004280813,0.000003716596],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009224778,"about_ca_system_score_gemma":0.00004110226,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001330584,"about_ca_topic_score_gemma":0.0004134197,"domain_scores_codex":[0.9990346,0.00002779498,0.0003753632,0.0001741042,0.0001276447,0.0002605002],"domain_scores_gemma":[0.9992573,0.000186469,0.0000513731,0.0002350521,0.0002031287,0.00006671281],"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":[0.0001555207,0.00003062985,0.001395377,0.0003189086,0.0001389773,5.104321e-7,0.0001242236,0.2961479,0.6859843,0.01438536,0.00004681628,0.001271543],"study_design_scores_gemma":[0.001529613,0.00006149091,0.000218703,0.00007868977,0.00001681148,2.945791e-7,0.00003309136,0.9944728,0.003325495,0.00007594419,0.00004147888,0.0001455889],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2690951,0.00004458705,0.7287173,0.00001799816,0.0001410511,0.0005034847,0.0002823578,0.0001047815,0.001093345],"genre_scores_gemma":[0.9993255,0.000001189333,0.0002925867,0.00001033338,0.00003654583,0.0001799036,0.000002875837,0.00002464218,0.0001264922],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7302303,"threshold_uncertainty_score":0.3936627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01903514769735667,"score_gpt":0.2003491246505603,"score_spread":0.1813139769532036,"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."}}