{"id":"W4385009849","doi":"10.2139/ssrn.4517118","title":"A Hybrid Model in a Nonlinear Disturbance Observer for Improving Compliance Error Compensation of Robotic Machining","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Compensation (psychology); Machining; Control theory (sociology); Nonlinear system; Observer (physics); Control engineering; Compliance (psychology); Disturbance (geology); Nonlinear model; Computer science; Engineering; Control (management); Artificial intelligence; Psychology; Mechanical engineering; Physics; Geology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004226233,0.0005195977,0.0006577583,0.0001992023,0.000285726,0.0007445425,0.0006361248,0.0007870357,0.001496048],"category_scores_gemma":[0.0006018872,0.00029886,0.0003984548,0.0002176033,0.0004614046,0.0006012961,0.000719631,0.0006906738,0.0003270722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002966803,"about_ca_system_score_gemma":0.0003925176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003981258,"about_ca_topic_score_gemma":0.004724318,"domain_scores_codex":[0.9997174,0.00006195131,0.00001826875,0.00006939264,0.0001031552,0.00002980706],"domain_scores_gemma":[0.9997432,0.00006560239,0.00004196956,0.00003621996,0.0001008431,0.00001227109],"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":[0.0004842936,0.0001739934,0.0009384705,0.000422805,0.0001139954,0.0001861628,0.0002071507,0.8124292,0.08219023,0.0108719,0.001451461,0.09053033],"study_design_scores_gemma":[0.00001264719,0.00008264025,0.0001627479,0.000004140694,0.00001091363,0.000008720309,0.000005516371,0.9967787,0.00207915,0.0003086602,0.000541612,0.000004622055],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0342246,0.0002141125,0.962289,0.00009402054,0.0001412354,0.00003859765,0.00002538534,0.0003349252,0.002638207],"genre_scores_gemma":[0.9510772,0.0001523792,0.04316092,0.00006062567,0.00004457085,0.00009244407,0.00005694874,0.00003424501,0.005320786],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003981258,"threshold_uncertainty_score":0.007916152,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.040230434148226,"score_gpt":0.2857482230061333,"score_spread":0.2455177888579073,"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."}}