{"id":"W2570882867","doi":"10.1109/cdc.2016.7799190","title":"Nonlinear model predictive control for trajectory tracking of an AUV: A distributed implementation","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Model predictive control; Trajectory; Kinematics; Control theory (sociology); Computer science; Tracking (education); Nonlinear system; Nonlinear model; Nonlinear programming; Control engineering; Control (management); Artificial intelligence; 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":[],"consensus_categories":[],"category_scores_codex":[0.00009377932,0.00009801851,0.0001632632,0.0000450386,0.00002241495,0.000006202024,0.00005986582,0.00004647969,0.00001121549],"category_scores_gemma":[0.00002437703,0.00007550636,0.00004405647,0.0000484193,0.00001162597,0.0004056328,0.000002619963,0.00002100912,5.528444e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009225362,"about_ca_system_score_gemma":0.00001739213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000468282,"about_ca_topic_score_gemma":0.00003734932,"domain_scores_codex":[0.9993415,0.00001392564,0.0002813332,0.0001200437,0.00008599868,0.0001572404],"domain_scores_gemma":[0.9995782,0.00007692778,0.00006060145,0.0001170607,0.0001245454,0.0000427118],"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":[0.00006866174,0.00001497892,0.00008157077,0.00002276275,0.0000474498,9.334754e-8,0.00008068764,0.9161851,0.0748753,0.0002671354,0.00003275711,0.008323476],"study_design_scores_gemma":[0.003167547,0.0001037746,0.0001898449,0.00001528975,0.00002848188,4.751313e-7,0.0001034484,0.9815798,0.01445545,0.0002291638,0.00003168735,0.00009503064],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02538801,0.00001390777,0.972443,0.00001885426,0.00005832609,0.0006836287,0.001099117,0.0002192499,0.00007585721],"genre_scores_gemma":[0.9760604,0.000002702331,0.02358298,0.000006996204,0.00005906461,0.0001494279,0.00009193887,0.0000282485,0.00001827442],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9506724,"threshold_uncertainty_score":0.307906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01061269703514326,"score_gpt":0.2596933684816807,"score_spread":0.2490806714465375,"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."}}