{"id":"W4384997514","doi":"10.1016/j.automatica.2023.111196","title":"Data-driven designs of observers and controllers via solving model matching problems","year":2023,"lang":"en","type":"article","venue":"Automatica","topic":"Control Systems and Identification","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"National Key Research and Development Program of China; Natural Sciences and Engineering Research Council of Canada; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China; University of Alberta","keywords":"Control theory (sociology); Computer science; Observer (physics); Data-driven; Realization (probability); Matching (statistics); Controller (irrigation); Control engineering; Kernel (algebra); Optimization problem; Control (management); Artificial intelligence; Engineering; Algorithm; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002542104,0.001073778,0.001237123,0.0007021875,0.0003961713,0.00125042,0.001423958,0.001858897,0.002537996],"category_scores_gemma":[0.007958764,0.0008825828,0.001071327,0.0005323726,0.0008841404,0.00150157,0.001984973,0.001618688,0.0006400778],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005912959,"about_ca_system_score_gemma":0.001338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001264313,"about_ca_topic_score_gemma":0.001105045,"domain_scores_codex":[0.9991542,0.0002643041,0.00005632765,0.0001866719,0.000255993,0.0000824779],"domain_scores_gemma":[0.9980369,0.0009837089,0.0002651702,0.0001995895,0.0004588146,0.00005586436],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001923824,0.000118877,0.0003727293,0.0003072235,0.00007385093,0.00008371048,0.0002036739,0.8201376,0.01088138,0.07828785,0.0009069385,0.08843381],"study_design_scores_gemma":[0.00002092594,0.00004678135,0.00003999769,0.00001335444,0.000007703153,0.00001143406,0.00001172831,0.9827752,0.002188018,0.01436669,0.0005109007,0.000007289116],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001814254,0.00002755769,0.9975868,0.00002640308,0.00001116105,0.00002212556,0.000007684731,0.00005975216,0.0004441596],"genre_scores_gemma":[0.6003217,0.0002344333,0.3954014,0.0001489493,0.00004297201,0.0004931411,0.0001686729,0.0001216065,0.003067263],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002542104,"threshold_uncertainty_score":0.01344413,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06027279366069063,"score_gpt":0.2494088282713116,"score_spread":0.1891360346106209,"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."}}