{"id":"W2792124445","doi":"10.1177/0954407018756557","title":"Model predictive control–based approach for assist wheel control of a multi-axle crane to improve steering efficiency and dynamic stability","year":2018,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering","topic":"Vehicle Dynamics and Control Systems","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Axle; Control theory (sociology); Stability (learning theory); MATLAB; Inertia; Weighting; Active steering; Model predictive control; Electronic stability control; Automobile handling; Automotive engineering; Computer science; Engineering; Control (management); Control engineering; Artificial intelligence","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":[0.0003269625,0.0006834265,0.0005701165,0.0003360536,0.000371071,0.0006654305,0.0007775238,0.0004464694,0.00126626],"category_scores_gemma":[0.0005047904,0.0002945589,0.0003794987,0.0002645663,0.000300366,0.0003964283,0.0004446802,0.0006141496,0.0002623794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000292591,"about_ca_system_score_gemma":0.0007811554,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006237019,"about_ca_topic_score_gemma":0.005794986,"domain_scores_codex":[0.9997898,0.00002872841,0.0000119866,0.00005157196,0.00008877061,0.00002910297],"domain_scores_gemma":[0.9998286,0.00004235349,0.00002352556,0.00001575935,0.00008113553,0.000008616894],"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":[0.0002258927,0.0001138536,0.0009885316,0.0004070069,0.00008626625,0.0002744573,0.0002007272,0.7465752,0.04392741,0.00687328,0.001581915,0.1987455],"study_design_scores_gemma":[0.00001364532,0.0001187961,0.0002336402,0.000009679768,0.00001589405,0.00003018079,0.00001163453,0.9946666,0.003226194,0.0004825408,0.001184127,0.000007072825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02328346,0.00045613,0.9711663,0.0001112696,0.00008159326,0.0000666352,0.00002174064,0.0005094743,0.004303386],"genre_scores_gemma":[0.9587848,0.000374538,0.03701778,0.00005822015,0.00003455261,0.0001051247,0.00004566223,0.0000203554,0.003558974],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006237019,"threshold_uncertainty_score":0.01240146,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007631549360038771,"score_gpt":0.2019535228684091,"score_spread":0.1943219735083704,"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."}}