{"id":"W2789408681","doi":"10.1109/tvt.2018.2872654","title":"Adaptive Tube-Based Nonlinear MPC for Economic Autonomous Cruise Control of Plug-In Hybrid Electric Vehicles","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Electric and Hybrid Vehicle Technologies","field":"Engineering","cited_by":75,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Control theory (sociology); Model predictive control; Cruise control; Estimator; Controller (irrigation); Nonlinear system; Adaptive control; Solver; Electric vehicle","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002331923,0.000548827,0.0004289408,0.0002177341,0.0002471009,0.0003897841,0.0006347969,0.0004489738,0.0009406055],"category_scores_gemma":[0.0004750343,0.0002350616,0.0003372202,0.0001790044,0.0003811166,0.0003800624,0.000482644,0.000592969,0.000170922],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003272004,"about_ca_system_score_gemma":0.0004453182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004091502,"about_ca_topic_score_gemma":0.002690103,"domain_scores_codex":[0.9998657,0.00003500164,0.000005083622,0.00002403887,0.00005472909,0.00001543297],"domain_scores_gemma":[0.9998463,0.00006249894,0.00003003786,0.00001007132,0.00004262711,0.000008347938],"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":[0.00005289059,0.00001565236,0.000214542,0.00004525314,0.00001521443,0.00004876982,0.00003580214,0.9750772,0.005513293,0.00281552,0.000296582,0.0158694],"study_design_scores_gemma":[0.000001463181,0.00002111097,0.00004531759,0.000001122294,0.000001210062,0.000003478962,0.000001294506,0.999135,0.0003770005,0.0001902576,0.0002211852,0.000001616254],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04120224,0.0001946155,0.9541054,0.00006605138,0.0000390331,0.00003122719,0.00002734079,0.0004295319,0.003904628],"genre_scores_gemma":[0.9742337,0.0001082571,0.02318691,0.00002663866,0.00001796423,0.00007898919,0.00005181848,0.00002324345,0.002272493],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004091502,"threshold_uncertainty_score":0.008135378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007858590541489423,"score_gpt":0.2115603589124682,"score_spread":0.2037017683709788,"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."}}