{"id":"W2143630778","doi":"10.1109/robot.2004.1307465","title":"CMAC adaptive control of flexible-joint robots using backstepping with tuning functions","year":2004,"lang":"en","type":"article","venue":"","topic":"Adaptive Control of Nonlinear Systems","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Backstepping; Payload (computing); Computer science; Control theory (sociology); Artificial neural network; Robot; Lyapunov function; Compensation (psychology); Adaptive control; Tracking error; Trajectory; Nonlinear system; Control engineering; Strict-feedback form; Artificial intelligence; Control (management); Engineering","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":[0.0003653623,0.000434253,0.0001843884,0.0002018819,0.0002633616,0.0002585533,0.000584243,0.000423965,0.0006472339],"category_scores_gemma":[0.0008299229,0.0001591928,0.000185051,0.000208441,0.0004654371,0.0002364596,0.0004513091,0.0004200467,0.0001569114],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000271588,"about_ca_system_score_gemma":0.0004045912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004978891,"about_ca_topic_score_gemma":0.004238203,"domain_scores_codex":[0.9998565,0.00002854905,0.000007194318,0.00002681587,0.00006501326,0.00001608273],"domain_scores_gemma":[0.999665,0.0001060151,0.00006432801,0.00004283217,0.0001029569,0.00001882037],"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":[0.0002074779,0.00007023229,0.0005783794,0.0001431951,0.00003861449,0.0001498275,0.0001046523,0.7458636,0.1056744,0.00724225,0.00105424,0.1388732],"study_design_scores_gemma":[0.00001429317,0.0001017924,0.00031108,0.000006664532,0.000004150639,0.00003178158,0.000004945797,0.9907864,0.007025137,0.0007406403,0.0009637332,0.000009308297],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07252265,0.0003036527,0.920242,0.0001243295,0.0001070211,0.00006834145,0.00001761946,0.0006735811,0.005940834],"genre_scores_gemma":[0.9356593,0.00008148094,0.06168465,0.00004354573,0.0000170307,0.0000928291,0.000020244,0.00001394345,0.002386896],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004978891,"threshold_uncertainty_score":0.009899855,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03246361163900808,"score_gpt":0.2174608784833227,"score_spread":0.1849972668443146,"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."}}