{"id":"W4297515215","doi":"10.1007/s10846-022-01742-w","title":"A Fuzzy Logic-based Cascade Control without Actuator Saturation for the Unmanned Underwater Vehicle Trajectory Tracking","year":2022,"lang":"en","type":"article","venue":"Journal of Intelligent & Robotic Systems","topic":"Adaptive Control of Nonlinear Systems","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Control theory (sociology); Kinematics; Actuator; Cascade; Backstepping; Robustness (evolution); Trajectory; Fuzzy logic; Engineering; Unmanned underwater vehicle; Control engineering; Torque; Underwater; Computer science; Adaptive control; Physics; Control (management); Artificial intelligence","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":[0.0004192283,0.0006191523,0.0006444975,0.000394696,0.0008450361,0.00085671,0.00118894,0.0007420576,0.002913313],"category_scores_gemma":[0.0004817113,0.0002992798,0.0005384932,0.0003304875,0.0004521236,0.0006832142,0.0006946685,0.0006334674,0.0003225417],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006299771,"about_ca_system_score_gemma":0.001063826,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01021053,"about_ca_topic_score_gemma":0.01087385,"domain_scores_codex":[0.999668,0.00002667076,0.00001973656,0.0001131504,0.0001260503,0.00004635473],"domain_scores_gemma":[0.9998084,0.00003159117,0.00002834081,0.00001327352,0.00009985818,0.00001859129],"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":[0.001147134,0.0004281646,0.001199434,0.0007224224,0.0001713673,0.0008029565,0.0005391486,0.5406344,0.1224802,0.02079064,0.005666289,0.3054179],"study_design_scores_gemma":[0.00004976349,0.0003188506,0.0003815214,0.00001435692,0.000028615,0.00004099278,0.00001862037,0.9930775,0.004269584,0.0008690357,0.0009164607,0.0000147554],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06958116,0.0005772374,0.9133128,0.0002391867,0.0004015809,0.0001721805,0.00007030951,0.0005864511,0.01505909],"genre_scores_gemma":[0.9690549,0.0001853613,0.02696404,0.00009348147,0.00004941146,0.00008707783,0.00005518548,0.0000127908,0.003497873],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01021053,"threshold_uncertainty_score":0.02030218,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03413415925585043,"score_gpt":0.2559574291648012,"score_spread":0.2218232699089508,"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."}}