{"id":"W3083570310","doi":"10.2316/j.2020.206-0449","title":"ROBUST ADAPTIVE CONTROL BASED ON MACHINE LEARNING AND NTSMC FOR WORKPIECE SURFACE-GRINDING ROBOT","year":2020,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Grinding; Computer science; Robot; Surface grinding; Adaptive control; Surface (topology); Control (management); Artificial intelligence; Engineering; Mechanical engineering; Mathematics; Geometry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003979666,0.0003970015,0.0005453086,0.0002012351,0.0003639089,0.0003883788,0.0007588569,0.0004569913,0.001401271],"category_scores_gemma":[0.0007063918,0.0002192981,0.0003489579,0.0002083029,0.0003989036,0.0003521896,0.0004077303,0.0005441401,0.0001500436],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004090132,"about_ca_system_score_gemma":0.0007298875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00923855,"about_ca_topic_score_gemma":0.007775734,"domain_scores_codex":[0.9996961,0.00004714642,0.00001679375,0.00008515567,0.000120709,0.00003408519],"domain_scores_gemma":[0.9996897,0.0001146396,0.00004837498,0.0000225769,0.0001136402,0.00001098841],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002169488,0.00006765972,0.00061787,0.0001822789,0.00005584991,0.00009625706,0.0001138759,0.837653,0.02762419,0.005357865,0.001098242,0.1269161],"study_design_scores_gemma":[0.000006560205,0.0000571355,0.0001682165,0.000002721162,0.000004632933,0.0000070148,0.000002841854,0.9980065,0.001222884,0.0002630492,0.000254878,0.000003576534],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04302121,0.0004067256,0.9518576,0.0001547893,0.0001064186,0.00003791647,0.00002284806,0.0004943753,0.003898066],"genre_scores_gemma":[0.9483913,0.0001227193,0.04875043,0.00005197051,0.00004144206,0.00007918484,0.00003832372,0.00002331672,0.002501244],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00923855,"threshold_uncertainty_score":0.01836956,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0181591777569744,"score_gpt":0.2371480092638636,"score_spread":0.2189888315068892,"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."}}