{"id":"W2785893031","doi":"10.1109/icus.2017.8278413","title":"Lyapunov-based model predictive control for dynamic positioning of autonomous underwater vehicles","year":2017,"lang":"en","type":"article","venue":"2017 IEEE International Conference on Unmanned Systems (ICUS)","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Control theory (sociology); Computer science; Controller (irrigation); Model predictive control; Lyapunov function; Stability (learning theory); Constraint (computer-aided design); Dynamic positioning; Control engineering; Control (management); Mathematical optimization; Engineering; Mathematics; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004348153,0.001019831,0.0006321619,0.0003353939,0.0003358624,0.0007323924,0.000878401,0.000538687,0.00158134],"category_scores_gemma":[0.0008153912,0.000311139,0.000383627,0.0004693106,0.0004972306,0.0005054594,0.0007179046,0.00104184,0.0004175233],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005549886,"about_ca_system_score_gemma":0.0008921361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004969903,"about_ca_topic_score_gemma":0.003687715,"domain_scores_codex":[0.9997584,0.0000589606,0.00001109323,0.00004118665,0.000106447,0.00002391826],"domain_scores_gemma":[0.9998226,0.00006887681,0.00003343385,0.00001194869,0.00005613835,0.000007006919],"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":[0.00002812153,0.00002201889,0.0001197576,0.0001623294,0.00002645787,0.00008768586,0.00005828345,0.9320495,0.003365176,0.02304403,0.001588483,0.03944812],"study_design_scores_gemma":[0.000004267152,0.0000255195,0.00002891638,0.000005151087,0.000003601316,0.000005951479,0.000003359275,0.9965814,0.0002789251,0.001980513,0.001079689,0.000002744169],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003369998,0.0007173261,0.9906095,0.000116332,0.00008326871,0.00002130442,0.00002699732,0.0002815413,0.004773685],"genre_scores_gemma":[0.9242617,0.001746152,0.06521861,0.0001386551,0.0001887307,0.0003014953,0.0002015683,0.00008841581,0.007854688],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004969903,"threshold_uncertainty_score":0.009881914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03608379473994495,"score_gpt":0.2900517409241224,"score_spread":0.2539679461841775,"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."}}