{"id":"W2312382299","doi":"10.2514/6.2016-0365","title":"Handling Hidden Coupling Terms in Gain-Scheduling Control Design: Application to a Pitch-Axis Missile Autopilot<br />","year":2016,"lang":"en","type":"article","venue":"AIAA Guidance, Navigation, and Control Conference","topic":"Stability and Control of Uncertain Systems","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Autopilot; Gain scheduling; Missile; Computer science; Automatic gain control; Control theory (sociology); Scheduling (production processes); Coupling (piping); Attitude control; Control (management); Engineering; Control engineering; Aerospace engineering; Artificial intelligence; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001047934,0.0003990476,0.0006821773,0.0001789287,0.0001905865,0.0001761641,0.0003520073,0.0001859872,0.00001843883],"category_scores_gemma":[0.0002140086,0.0003417355,0.00008458205,0.0003301454,0.000107057,0.0003587067,0.00002299549,0.0001825717,0.00004159159],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000208739,"about_ca_system_score_gemma":0.0001234307,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000966779,"about_ca_topic_score_gemma":0.00009029929,"domain_scores_codex":[0.9974352,0.0001060534,0.0009178965,0.0006331269,0.000311314,0.0005964625],"domain_scores_gemma":[0.9981043,0.0007068198,0.0001559945,0.0004901869,0.0003173306,0.0002253512],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000620479,0.00009959734,0.04709191,0.0003443823,0.0002372906,0.00001449046,0.001522004,0.07518007,0.7604492,0.01277871,0.0001321974,0.1015297],"study_design_scores_gemma":[0.01029757,0.0001918387,0.02477944,0.001668002,0.00009266167,0.00001094401,0.0002215619,0.9464144,0.004303116,0.006937785,0.004143337,0.0009394173],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2205194,0.001491732,0.7749819,0.0008118959,0.0002416713,0.001310739,0.00002689271,0.0002656104,0.0003500906],"genre_scores_gemma":[0.9973149,0.00007770455,0.001137107,0.0002638542,0.0002111962,0.0008552601,0.000006736586,0.00004484188,0.00008840875],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8712342,"threshold_uncertainty_score":0.9999034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01268659462997339,"score_gpt":0.2243554181654637,"score_spread":0.2116688235354903,"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."}}