{"id":"W2075233922","doi":"10.3182/20120620-3-dk-2025.00159","title":"On the Design of Hybrid Robust Output Regulation for the METIS Cold Chopper","year":2012,"lang":"en","type":"article","venue":"IFAC Proceedings Volumes","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"ASTRON; Ministry of Economy, Trade and Industry; Rijksuniversiteit Groningen","keywords":"Metis; Chopper; Spectrograph; Feed forward; Controller (irrigation); Control theory (sociology); Engineering; Control engineering; Computer science; Control (management); Physics; Electrical engineering; Artificial intelligence; Astronomy","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009756595,0.0007946698,0.001072394,0.0003132376,0.0005166156,0.001200785,0.0008521455,0.0008733169,0.002981509],"category_scores_gemma":[0.001111447,0.0003664308,0.0005663081,0.0002970964,0.0006946056,0.0004584285,0.0009045631,0.0006298651,0.0003578943],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005240541,"about_ca_system_score_gemma":0.0005923146,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00204412,"about_ca_topic_score_gemma":0.001921884,"domain_scores_codex":[0.9995734,0.0001113505,0.00001462817,0.0001048457,0.0001492106,0.00004653495],"domain_scores_gemma":[0.9996916,0.0001358932,0.00004492913,0.00002241783,0.00009556933,0.000009598533],"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":[0.0002360577,0.00003144774,0.0002179869,0.0002742868,0.00006874088,0.0001301959,0.0001556154,0.9123831,0.02098,0.01881339,0.0011958,0.04551343],"study_design_scores_gemma":[0.00001569983,0.0001516432,0.0001316053,0.00001383803,0.00001751192,0.00002641053,0.00001852226,0.9928034,0.002956083,0.002386728,0.001467865,0.00001067769],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01420831,0.0003716378,0.9784181,0.000150397,0.00005158477,0.00005239431,0.00002639299,0.0001892887,0.006532002],"genre_scores_gemma":[0.9326363,0.0004276629,0.06080363,0.0001326532,0.00006707769,0.0001558494,0.00006023457,0.00007316702,0.005643474],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002981509,"threshold_uncertainty_score":0.009974182,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02399044508263142,"score_gpt":0.2058146236026442,"score_spread":0.1818241785200128,"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."}}