{"id":"W4389584680","doi":"10.1109/tii.2023.3331772","title":"Trajectory Tracking Control of Autonomous Underwater Vehicles Using Improved Tube-Based Model Predictive Control Approach","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Adaptive Control of Nonlinear Systems","field":"Engineering","cited_by":76,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria; Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Control theory (sociology); Trajectory; Model predictive control; Controller (irrigation); Control engineering; Nonlinear system; Computer science; Tracking (education); Control system; Vehicle dynamics; Engineering; 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":[0.0003044916,0.000533714,0.0005889303,0.0002751644,0.0003117571,0.0004733756,0.0007170957,0.0005938609,0.0009223788],"category_scores_gemma":[0.0004958241,0.0002246765,0.0004482746,0.0002965833,0.0004763083,0.0005746158,0.0006995808,0.0005962342,0.0001674558],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003984685,"about_ca_system_score_gemma":0.0005520431,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005346754,"about_ca_topic_score_gemma":0.002457076,"domain_scores_codex":[0.99983,0.00003748425,0.000008120899,0.00003411174,0.00007249283,0.00001774771],"domain_scores_gemma":[0.9998133,0.0000601238,0.00004349645,0.00001394282,0.00005935102,0.000009848827],"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":[0.00002764586,0.000007459498,0.0001443816,0.0000328104,0.00001053697,0.00004337165,0.00003694168,0.980162,0.00402243,0.00313695,0.0002513714,0.01212413],"study_design_scores_gemma":[0.000001325861,0.00001872782,0.00002545208,0.000001346889,0.000001498363,0.000003211802,0.000001367558,0.9992198,0.0003047382,0.0002479707,0.0001728671,0.000001761726],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0191578,0.0002127395,0.9777125,0.00007167607,0.00002575937,0.00001712412,0.00001925039,0.0003027978,0.002480373],"genre_scores_gemma":[0.9516204,0.0002831133,0.04492636,0.00004448735,0.00002730002,0.0001011793,0.0000751977,0.0000359472,0.002885952],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005346754,"threshold_uncertainty_score":0.01063126,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05974039118051874,"score_gpt":0.2426573411600818,"score_spread":0.182916949979563,"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."}}