{"id":"W1553158584","doi":"10.1002/acs.2400","title":"Neural adaptive control for leader–follower flocking of networked nonholonomic agents with unknown nonlinear dynamics","year":2013,"lang":"en","type":"article","venue":"International Journal of Adaptive Control and Signal Processing","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Flocking (texture); Nonholonomic system; Control theory (sociology); Nonlinear system; Computer science; Control engineering; Adaptive control; Artificial neural network; Dynamics (music); Control (management); Engineering; Robot; Mobile robot; Artificial intelligence; Psychology; Physics","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":[0.0003323279,0.0003370709,0.0002708407,0.0002068285,0.0002964759,0.0003462323,0.0004740011,0.0004275008,0.0006995058],"category_scores_gemma":[0.0008094234,0.0001419362,0.0001860155,0.0001556718,0.0004396026,0.0003066857,0.0004957963,0.0004016665,0.00006590504],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002980667,"about_ca_system_score_gemma":0.0002689945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005217829,"about_ca_topic_score_gemma":0.003386822,"domain_scores_codex":[0.9998994,0.00002323779,0.000006542129,0.00002852458,0.00002662014,0.0000156844],"domain_scores_gemma":[0.9997494,0.0001022299,0.00007312326,0.00001078971,0.0000500694,0.00001442554],"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":[0.00004721616,0.00002471929,0.0003546565,0.00004978001,0.00001994377,0.00009995545,0.00007151305,0.9701201,0.005021819,0.004385578,0.0002330021,0.01957172],"study_design_scores_gemma":[0.000004160507,0.00002234414,0.00009072856,0.000001487122,0.000002707851,0.000004138094,0.000005394699,0.9988828,0.0001763547,0.0007236886,0.00008432542,0.000001847929],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1910495,0.0005358068,0.8002483,0.0003134238,0.0001112523,0.00004752445,0.00002036194,0.0001756596,0.007498121],"genre_scores_gemma":[0.9903288,0.000106264,0.00761277,0.0000307302,0.00001958491,0.00003344784,0.000009656102,0.000004743099,0.001853988],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005217829,"threshold_uncertainty_score":0.0103749,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01713991223825148,"score_gpt":0.2429895945215603,"score_spread":0.2258496822833088,"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."}}