{"id":"W2074472778","doi":"10.1109/tim.2014.2310093","title":"Analysis and Compensation of Delays in FF H1 Fieldbus Control Loop Using Model Predictive Control","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fieldbus; Model predictive control; Control theory (sociology); Compensation (psychology); Foundation Fieldbus H1; Control engineering; Control system; Networked control system; Test bench; PID controller; Engineering; Transmission (telecommunications); Controller (irrigation); Feedback loop; FOUNDATION fieldbus; Computer science; Control (management); 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002510135,0.0001226938,0.0002477144,0.0002998661,0.0000557821,0.00001588828,0.00002635804,0.00006071125,0.000005566579],"category_scores_gemma":[0.000004980163,0.000132872,0.00004140495,0.0001790534,0.00002461295,0.0001825289,2.683462e-7,0.00008348032,2.922647e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001490283,"about_ca_system_score_gemma":0.00001280688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006028646,"about_ca_topic_score_gemma":0.0002393707,"domain_scores_codex":[0.9990882,0.00006584332,0.0003439468,0.0001524562,0.0002380456,0.0001114997],"domain_scores_gemma":[0.9996493,0.00003841048,0.00007830536,0.00009264846,0.00009199203,0.00004936118],"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":[0.0000841407,0.00002747973,0.0009922225,0.00002842317,0.0002212648,7.643698e-8,0.0002313685,0.9750724,0.01502501,0.00002922252,2.84483e-7,0.008288059],"study_design_scores_gemma":[0.003379024,0.00006907649,0.001823183,0.00003516006,0.0003525465,8.695197e-7,0.00012832,0.9891165,0.00495159,0.00004079453,8.127937e-7,0.0001020823],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1449584,0.00002969228,0.8544117,0.00002206646,0.00008849845,0.0003616067,0.00001990786,0.00003412948,0.00007395601],"genre_scores_gemma":[0.9977237,0.00003357003,0.002123478,0.00004511568,0.000007429751,0.00005045789,0.000002642373,0.00001135595,0.00000226506],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8527653,"threshold_uncertainty_score":0.5418363,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01725001490197458,"score_gpt":0.2205939285190897,"score_spread":0.2033439136171151,"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."}}