{"id":"W4388562214","doi":"10.1109/icstcc59206.2023.10308491","title":"Stochastic Model Predictive Control with Dynamic Chance Constraints","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"CODE","keywords":"Mathematical optimization; Model predictive control; Probabilistic logic; Constraint (computer-aided design); Computer science; Stochastic optimization; Constraint satisfaction; Controller (irrigation); Stochastic control; Optimal control; Control (management); Control theory (sociology); Mathematics; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000505598,0.0001314698,0.0001636468,0.0000804088,0.00003369219,0.00001240701,0.00006940463,0.00004563989,0.0000117704],"category_scores_gemma":[0.00001597496,0.0001147019,0.00001845191,0.0002114786,0.0000464467,0.000147532,0.000006315529,0.00007857847,0.00006869014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000742777,"about_ca_system_score_gemma":0.00001681372,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":9.174205e-7,"about_ca_topic_score_gemma":0.00001117895,"domain_scores_codex":[0.9993324,0.000007402145,0.0001342249,0.0001522532,0.0001287952,0.0002449644],"domain_scores_gemma":[0.9996771,0.00004860962,0.00002299214,0.0001445627,0.00005081375,0.00005595779],"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":[0.00001903577,0.000002374927,0.000006560821,0.00001265479,0.00004421462,0.000002795848,0.00007430941,0.9977179,0.0009058026,0.0005304511,0.00003358687,0.0006503655],"study_design_scores_gemma":[0.001085806,0.00002936162,0.0000733054,0.00003095666,0.00001425416,0.000005025188,0.00007957027,0.9982386,0.00001839099,0.0002904013,0.000001671563,0.0001326952],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002401673,0.00003040199,0.9921936,0.00003568385,0.0000896185,0.0004496834,0.00004208384,0.001562007,0.003195232],"genre_scores_gemma":[0.9970682,0.000004717659,0.002239934,0.00002260644,0.0000191045,0.0001572322,0.00001368258,0.00004238241,0.0004321449],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9946665,"threshold_uncertainty_score":0.4677407,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003903465531000087,"score_gpt":0.1925458618251708,"score_spread":0.1886423962941707,"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."}}