{"id":"W4322832136","doi":"10.48550/arxiv.2303.00251","title":"Distributed Data-driven Predictive Control via Dissipative Behavior Synthesis","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Dissipative system; Control theory (sociology); Model predictive control; Computer science; Stability (learning theory); Data-driven; Set (abstract data type); Process (computing); Quadratic equation; Function (biology); Engineering design process; LTI system theory; Control (management); Control engineering; Engineering; Linear system; Mathematics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001456103,0.0005046368,0.0006752213,0.0002322311,0.0001224109,0.0000532264,0.001181765,0.0004362146,0.00003349344],"category_scores_gemma":[0.000165626,0.0006167627,0.0001601104,0.0004381187,0.0001039494,0.0004520773,0.0007261835,0.0006113101,0.0001239467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000630262,"about_ca_system_score_gemma":0.00006067572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001054486,"about_ca_topic_score_gemma":0.00009111447,"domain_scores_codex":[0.9978538,0.0001543343,0.0003535022,0.001070099,0.0001234796,0.0004447771],"domain_scores_gemma":[0.9973748,0.0003641807,0.0002410756,0.001640519,0.0001812758,0.0001981443],"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":[0.00006214531,0.00003985913,0.002535498,0.00009005348,0.0005047934,0.0002155451,0.00007491284,0.9958825,0.00005786776,0.0001711035,0.0001738036,0.0001919148],"study_design_scores_gemma":[0.0006684715,0.00002102988,0.004615836,0.000162756,0.0008944146,0.000001795119,0.00008699804,0.9924102,0.00003220221,0.0004630819,0.00009882882,0.0005444185],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01927098,0.00004881176,0.9676839,0.00001738476,0.0008242481,0.001215258,0.009033265,0.001699393,0.0002067391],"genre_scores_gemma":[0.9971803,0.0001145773,0.0003203458,0.000004351762,0.0001482054,0.00005171517,0.001864053,0.0001288695,0.0001875301],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9779094,"threshold_uncertainty_score":0.9996284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06276544516957255,"score_gpt":0.195170192134703,"score_spread":0.1324047469651305,"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."}}