{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004638376,0.0003569938,0.0006316417,0.0004983321,0.0001411077,0.00005890091,0.0002254881,0.0004437159,0.000006900972],"category_scores_gemma":[0.00001621394,0.0003493939,0.0002592132,0.0004195458,0.0001067794,0.000463806,0.00000107044,0.0006993075,0.00001366924],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003276185,"about_ca_system_score_gemma":0.0002526223,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002460483,"about_ca_topic_score_gemma":0.000005908926,"domain_scores_codex":[0.9978237,0.00007639117,0.001125186,0.0001466517,0.0003577554,0.0004703288],"domain_scores_gemma":[0.998875,0.00030491,0.0002147002,0.0003106479,0.000156529,0.0001382115],"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":[0.0002719086,0.00006734239,0.000007958328,0.00009495614,0.000326261,9.446882e-7,0.0006644385,0.9890914,0.007364376,0.00000489781,0.00003622603,0.00206929],"study_design_scores_gemma":[0.007034069,0.0001794959,0.000007413526,0.00008616108,0.0002223342,0.000003402194,0.0006700285,0.9806387,0.01082464,0.0000068177,0.00002817306,0.0002987604],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06050761,0.00001127196,0.9361985,0.000015883,0.0007047972,0.001074019,0.0006773217,0.0006068397,0.0002037541],"genre_scores_gemma":[0.9975292,0.000002449842,0.00204165,0.00004797728,0.0001665061,0.00009469216,0.00001413522,0.00007510217,0.00002825977],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9370216,"threshold_uncertainty_score":0.9998958,"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."}}