{"id":"W3047893912","doi":"10.1016/j.automatica.2020.109185","title":"Design of data-driven PID controllers with adaptive updating rules","year":2020,"lang":"en","type":"article","venue":"Automatica","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":67,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Cabinet Office, Government of Japan","keywords":"PID controller; Control theory (sociology); Stability (learning theory); Linearization; Convergence (economics); Nonlinear system; Adaptive control; Computer science; Set (abstract data type); Lyapunov function; Feedback linearization; Control engineering; Mathematics; Control (management); Engineering; Artificial intelligence; Temperature control","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":[0.0007813617,0.0006451563,0.0006295408,0.000424837,0.0003274079,0.001098322,0.001319779,0.000885256,0.001433234],"category_scores_gemma":[0.001943028,0.0005213152,0.0003964773,0.0003072137,0.0004124408,0.0004600315,0.0006803494,0.0009059,0.0004919895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004289504,"about_ca_system_score_gemma":0.0009322375,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001472823,"about_ca_topic_score_gemma":0.001592543,"domain_scores_codex":[0.9996623,0.00004916238,0.00002255193,0.00007816726,0.0001501174,0.00003776413],"domain_scores_gemma":[0.9994765,0.0001570879,0.00006478027,0.00004987376,0.0002295429,0.00002217146],"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":[0.000252246,0.00014971,0.0007025092,0.0003490997,0.00008515725,0.0001533515,0.0001388671,0.7152411,0.03370079,0.01690249,0.001760856,0.2305638],"study_design_scores_gemma":[0.00003117609,0.00006430203,0.0001208512,0.000008823365,0.00001161733,0.00002561456,0.000005370958,0.9911342,0.005654479,0.001683343,0.001251537,0.000008673658],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006273021,0.0001764097,0.9910538,0.00004008222,0.00005500767,0.0000650492,0.00001818988,0.000301599,0.002016812],"genre_scores_gemma":[0.6912057,0.0002769491,0.3045647,0.000117945,0.00005215578,0.0004332776,0.0001065254,0.00007917307,0.003163553],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001472823,"threshold_uncertainty_score":0.004794598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03362861556682634,"score_gpt":0.2281974491613242,"score_spread":0.1945688335944979,"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."}}