{"id":"W3112369266","doi":"10.1109/syscon47679.2020.9275891","title":"Neuro-Fuzzy Controller Based on Model Predictive Control for a Nonlinear Underactuated Mechanical System","year":2020,"lang":"en","type":"article","venue":"2020 IEEE International Systems Conference (SysCon)","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Mitacs","keywords":"Control theory (sociology); Underactuation; Nonlinear system; Nonlinear model; Model predictive control; Computer science; Control engineering; Controller (irrigation); Fuzzy logic; Fuzzy control system; Nonlinear dynamical systems; Control (management); Engineering; Artificial intelligence; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002749519,0.0005322413,0.0008791653,0.000137945,0.0001080913,0.0002451565,0.0006032845,0.0002732012,0.00002581026],"category_scores_gemma":[0.0003850001,0.0005283895,0.0002453335,0.0001875474,0.00003573524,0.0003621715,0.00002160653,0.0003757778,0.0001672836],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000468066,"about_ca_system_score_gemma":0.0001581353,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001794248,"about_ca_topic_score_gemma":0.000005513311,"domain_scores_codex":[0.9968588,0.0001486444,0.001067069,0.0007232025,0.0007286327,0.0004736786],"domain_scores_gemma":[0.9975525,0.0006642841,0.0003342444,0.0002992042,0.0008131958,0.0003366425],"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":[0.000877089,0.00003580044,0.00001423418,0.0002132517,0.0003299318,0.00001437215,0.00007166904,0.9785894,0.007777851,0.01047406,0.001483308,0.0001191012],"study_design_scores_gemma":[0.006594748,0.0002832039,0.000003632768,0.0003195364,0.0000898277,0.00001001219,0.0001671821,0.9907462,0.0003571327,0.00008766662,0.0008998233,0.0004410544],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0006637822,0.00004012193,0.9873051,0.001285487,0.002517024,0.002806423,0.001477528,0.001031035,0.002873493],"genre_scores_gemma":[0.994803,0.000006126867,0.001814099,0.0007022702,0.00109433,0.001117905,0.0002262719,0.0001336915,0.0001023615],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9941391,"threshold_uncertainty_score":0.9997168,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0239524764092135,"score_gpt":0.2414908893348217,"score_spread":0.2175384129256082,"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."}}