{"id":"W2907089789","doi":"10.1109/iecon.2018.8591617","title":"Multi-Layered Formation Control of Autonomous Marine Vehicles with Nonlinear Dynamics","year":2018,"lang":"en","type":"article","venue":"","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Lyapunov function; Control theory (sociology); Nonlinear system; Convergence (economics); Drag; Exponential stability; Acceleration; Lyapunov stability; Control-Lyapunov function; Vehicle dynamics; Lyapunov redesign; Rate of convergence; Computer science; Mathematics; Engineering; Control (management); Physics; Classical mechanics; Key (lock); Aerospace 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":[0.0002471847,0.000161002,0.0002698401,0.0000958469,0.00007516664,0.00009253529,0.0006605241,0.00006594611,0.00001249873],"category_scores_gemma":[0.00003107306,0.0001231602,0.00005306861,0.0002598292,0.00008157083,0.0006269175,0.0001289598,0.00006914374,0.00008077179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001011598,"about_ca_system_score_gemma":0.00007190871,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002542092,"about_ca_topic_score_gemma":0.0005944177,"domain_scores_codex":[0.998712,0.00005865336,0.0004144726,0.0002661925,0.0002714782,0.0002772218],"domain_scores_gemma":[0.9986074,0.00005814439,0.0002602859,0.0005916121,0.0003946728,0.00008788236],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00125524,0.003676737,0.1142294,0.0005988978,0.001422242,0.0001216029,0.003533819,0.008586228,0.06142095,0.2347062,0.00130127,0.5691473],"study_design_scores_gemma":[0.003081036,0.0002686564,0.004924313,0.00001698644,0.00001153211,0.0000176582,0.00004766428,0.9883242,0.002781656,0.00004174421,0.000329249,0.0001553442],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02842007,0.000008093855,0.9689552,0.0004940874,0.0001554061,0.0004109939,0.00003301252,0.0002231516,0.001300034],"genre_scores_gemma":[0.8839986,7.402505e-7,0.1155284,0.0001091965,0.00005177744,0.00001655426,0.0000253188,0.000009355816,0.0002599504],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9797379,"threshold_uncertainty_score":0.5022327,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01137405593455528,"score_gpt":0.2241614918872867,"score_spread":0.2127874359527314,"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."}}