{"id":"W2001959966","doi":"10.1115/msec_icmp2008-72374","title":"Force/Velocity Control of a Pneumatic Gantry Robot for Contour Tracking With Neural Network Compensation","year":2008,"lang":"en","type":"article","venue":"","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Control theory (sociology); Compensation (psychology); Controller (irrigation); Tracking (education); Lag; Robot; Artificial neural network; PID controller; Computer science; Engineering; Control engineering; Artificial intelligence; Control (management)","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":[0.0002670827,0.0002483506,0.0001646928,0.0000994383,0.0001658321,0.0002229208,0.0003059297,0.0002731406,0.001032945],"category_scores_gemma":[0.0004271391,0.0001143908,0.0001126333,0.0001222811,0.0002306056,0.0002264968,0.0001921126,0.000245917,0.0001586554],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001467645,"about_ca_system_score_gemma":0.0002267264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001442309,"about_ca_topic_score_gemma":0.001874185,"domain_scores_codex":[0.999903,0.00001340476,0.000004256264,0.00002111052,0.00005139485,0.000006853411],"domain_scores_gemma":[0.9998679,0.00004250657,0.00002984784,0.00001546998,0.0000372548,0.000007024071],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003898944,0.00009955481,0.001250175,0.0002450432,0.00002874595,0.0002460132,0.0001770607,0.2321423,0.3591548,0.003361813,0.001227949,0.4016767],"study_design_scores_gemma":[0.00004347055,0.0004039182,0.002108051,0.00001402442,0.00001617219,0.0001628487,0.00001443092,0.9548453,0.03858522,0.000590319,0.003199452,0.00001684319],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1496907,0.0003753277,0.8425403,0.0001356304,0.00008811088,0.00009390721,0.00001619514,0.0006047597,0.006455016],"genre_scores_gemma":[0.9239539,0.00009814747,0.07335061,0.0000426949,0.00001806885,0.00004143541,0.00001639245,0.00001347964,0.002465232],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001442309,"threshold_uncertainty_score":0.003455579,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02920130942270047,"score_gpt":0.2214782963058774,"score_spread":0.1922769868831769,"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."}}