{"id":"W3022912303","doi":"10.1109/tcst.2020.2990532","title":"A Novel Attitude-Tracking Control for Spacecraft Networks With Input Delays","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Control Systems Technology","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Control theory (sociology); Controller (irrigation); Fuzzy logic; Compensation (psychology); Tracking (education); Stability (learning theory); Spacecraft; Attitude control; Fuzzy control system; Computer science; Nonlinear system; Control (management); Control engineering; Engineering; Artificial intelligence; Physics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002210527,0.000468856,0.0002610661,0.0001759813,0.0003213428,0.0004839847,0.0006361227,0.0004834648,0.0009696235],"category_scores_gemma":[0.0002947904,0.0001169547,0.0002417059,0.0002723758,0.0002667661,0.0004585764,0.0004206903,0.0004646582,0.0001716385],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003328257,"about_ca_system_score_gemma":0.00040052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001967113,"about_ca_topic_score_gemma":0.002219408,"domain_scores_codex":[0.9998642,0.00001349416,0.00000778255,0.00004831721,0.00004872776,0.00001749024],"domain_scores_gemma":[0.9999139,0.00001714858,0.00002062447,0.000006376392,0.00003265496,0.000009219278],"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":[0.0002491688,0.0001034565,0.0007433321,0.0004830185,0.00007392576,0.0004960934,0.0003304393,0.6021861,0.1149872,0.04887186,0.002620067,0.2288553],"study_design_scores_gemma":[0.00003506906,0.0002078991,0.0002256866,0.00001477403,0.00001869938,0.00007873664,0.00001846764,0.9864776,0.006742279,0.002253325,0.003916001,0.00001141251],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02296486,0.0004171877,0.9727864,0.00007990727,0.000146331,0.00003451895,0.00002995454,0.0001947825,0.003346006],"genre_scores_gemma":[0.9231415,0.0006155217,0.07040552,0.00009357651,0.00008771355,0.0001200321,0.00007195221,0.00001621112,0.005447941],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001967113,"threshold_uncertainty_score":0.003911376,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0183405678382365,"score_gpt":0.2306354790881148,"score_spread":0.2122949112498783,"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."}}