{"id":"W2094358048","doi":"10.1109/acc.2012.6315409","title":"Offset-free model predictive controller for Vapor Compression Cycle","year":2012,"lang":"en","type":"article","venue":"","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Air Conditioning and Refrigeration Center","keywords":"Control theory (sociology); Model predictive control; Offset (computer science); Decoupling (probability); Computer science; Vapor-compression refrigeration; Linear model; State observer; Control engineering; Engineering; Nonlinear system; Control (management); Artificial intelligence","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":[0.0002104624,0.0004726325,0.0004047007,0.0002138199,0.0003770435,0.0005775564,0.0007464151,0.00036731,0.001671675],"category_scores_gemma":[0.0004597516,0.0001741982,0.0002167395,0.0002186397,0.0002982213,0.0002990641,0.000514661,0.0006922157,0.0002979764],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004432954,"about_ca_system_score_gemma":0.0006609415,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00463549,"about_ca_topic_score_gemma":0.004590997,"domain_scores_codex":[0.9997795,0.00002859159,0.000007512288,0.00004243639,0.0001173812,0.00002461327],"domain_scores_gemma":[0.9998624,0.00004180965,0.00002423641,0.00001464645,0.00005066354,0.00000626279],"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":[0.0001984556,0.00008091816,0.0005109335,0.0002599454,0.00003566763,0.0001680638,0.00009381435,0.851482,0.0275953,0.008289687,0.002246621,0.1090385],"study_design_scores_gemma":[0.00001872187,0.00008270746,0.00022503,0.000007208168,0.000007661056,0.00001815437,0.000004086348,0.9937608,0.003727772,0.0006880828,0.001452661,0.000007154946],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0659604,0.0008872365,0.9116074,0.0001912799,0.000174793,0.00009628041,0.000107565,0.002274303,0.01870063],"genre_scores_gemma":[0.9816692,0.000140997,0.01465829,0.00004290239,0.00002148083,0.00006281921,0.00006919345,0.00002628587,0.003308725],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00463549,"threshold_uncertainty_score":0.009217024,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009128330085113065,"score_gpt":0.2201707100381749,"score_spread":0.2110423799530618,"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."}}