{"id":"W2772869917","doi":"10.1109/iros.2017.8206089","title":"Industrial-scale autonomous wheeled-vehicle path following by combining iterative learning control with feedback linearization","year":2017,"lang":"en","type":"article","venue":"","topic":"Iterative Learning Control Systems","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Iterative learning control; Feedback linearization; Control theory (sociology); Linearization; Computer science; Control engineering; Nonlinear system; Path (computing); Computation; Trajectory; Control (management); Nonlinear control; Scale (ratio); Field (mathematics); Vehicle dynamics; Engineering; Artificial intelligence; Mathematics; Algorithm; Aerospace 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","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0004411936,0.0003546752,0.0005297023,0.00007955632,0.0008755841,0.001050939,0.0003030347,0.0002124063,0.00005390193],"category_scores_gemma":[0.0001661531,0.0003118719,0.00009909413,0.00009989691,0.00005830172,0.0008446393,0.00003982546,0.0007219457,0.00008617085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001439005,"about_ca_system_score_gemma":0.00004023032,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000114465,"about_ca_topic_score_gemma":0.00002860173,"domain_scores_codex":[0.9982514,0.0001869146,0.0004063618,0.0003676095,0.0002965752,0.0004911529],"domain_scores_gemma":[0.99897,0.0001513772,0.0002270048,0.0004098445,0.0001055875,0.0001362065],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002159995,0.0001084883,0.6108735,0.00007710778,0.001074499,0.00008270882,0.007583045,0.2388575,0.1230575,0.0004539904,0.003260381,0.01435527],"study_design_scores_gemma":[0.009782869,0.0004315382,0.005706678,0.0003351848,0.00008782062,0.000008138078,0.0005265326,0.9681298,0.004849314,0.000009396626,0.009350093,0.0007826466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8280452,0.0001762119,0.1444275,0.0003118513,0.0007654267,0.0008729069,0.00001369446,0.001143344,0.02424391],"genre_scores_gemma":[0.9951274,0.000001962038,0.0004276904,0.00005992189,0.0002578844,0.00006457804,0.00004129337,0.0001015336,0.003917704],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7292723,"threshold_uncertainty_score":0.9999861,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00760537425939948,"score_gpt":0.2037191826372981,"score_spread":0.1961138083778987,"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."}}