{"id":"W2034217323","doi":"10.1016/j.ast.2014.12.031","title":"Tracking control of spacecraft formation flying with collision avoidance","year":2015,"lang":"en","type":"article","venue":"Aerospace Science and Technology","topic":"Spacecraft Dynamics and Control","field":"Engineering","cited_by":133,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Specialized Research Fund for the Doctoral Program of Higher Education of China; Program for New Century Excellent Talents in University; National Natural Science Foundation of China; Heilongjiang Youth Development Foundation","keywords":"Collision avoidance; Spacecraft; Control theory (sociology); Obstacle avoidance; Lyapunov function; Parametric statistics; Tracking (education); Obstacle; Computer science; Nonlinear system; Control engineering; Control (management); Collision; Engineering; Aerospace engineering; Mathematics; Artificial intelligence; Mobile robot; Physics; Robot","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":[0.0002446166,0.0003056955,0.0002974308,0.0002309907,0.0004752784,0.0005531578,0.0004630031,0.000290547,0.001317322],"category_scores_gemma":[0.0005982381,0.0001505516,0.0001961273,0.000292301,0.0003912279,0.0002767575,0.0006306153,0.0003253022,0.0001627049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003363318,"about_ca_system_score_gemma":0.000593464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007841334,"about_ca_topic_score_gemma":0.004574438,"domain_scores_codex":[0.9999021,0.00001949789,0.000003241842,0.00001994353,0.00003601058,0.00001925657],"domain_scores_gemma":[0.999887,0.00003417291,0.00002665753,0.00000913395,0.00003289632,0.00001004246],"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":[0.0001909806,0.00004924121,0.0005749291,0.00007100886,0.00003738152,0.0000794899,0.0001665336,0.8962744,0.009559657,0.0235357,0.0009577625,0.06850288],"study_design_scores_gemma":[0.00001834385,0.00008002094,0.0002938744,0.000003672274,0.000006433448,0.00001328323,0.00001201869,0.9951563,0.0008682725,0.00288079,0.0006624713,0.000004527359],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1334592,0.000406265,0.8518435,0.0002104381,0.0001480295,0.00005223279,0.00004033424,0.0002056773,0.01363436],"genre_scores_gemma":[0.9871451,0.0000969554,0.009846158,0.00002164721,0.00002034123,0.00002686455,0.00001767276,0.000008119874,0.002817144],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007841334,"threshold_uncertainty_score":0.01559144,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00621323682029414,"score_gpt":0.1937701431819612,"score_spread":0.1875569063616671,"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."}}