{"id":"W1839395960","doi":"10.1109/ccece.1993.332428","title":"Compensation of sampled-data robot adaptive controllers for improved stability","year":2002,"lang":"en","type":"article","venue":"","topic":"Adaptive Control of Nonlinear Systems","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Control theory (sociology); Adaptive control; Discretization; Stability (learning theory); Compensation (psychology); Computer science; Controller (irrigation); Lyapunov function; Norm (philosophy); Lyapunov stability; Nonlinear system; Mathematics; Artificial intelligence; Control (management); Machine learning","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":[0.0004123771,0.0003103136,0.0002840347,0.0002037564,0.0002420015,0.0004387728,0.0004912892,0.0002952517,0.001581554],"category_scores_gemma":[0.001474436,0.0001582905,0.0002104711,0.0001372059,0.0002606003,0.0004128268,0.0002787949,0.0004392882,0.0002266093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002964728,"about_ca_system_score_gemma":0.0003378018,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008915514,"about_ca_topic_score_gemma":0.001309568,"domain_scores_codex":[0.9997249,0.00004870327,0.00002197314,0.00004461989,0.0001464348,0.00001337434],"domain_scores_gemma":[0.9994938,0.0001656365,0.00007448546,0.00008480732,0.0001673337,0.00001400073],"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":[0.0006241092,0.0002023403,0.001760853,0.0004876897,0.00008219441,0.0003278252,0.0003826325,0.3730184,0.2918592,0.02970865,0.002384355,0.2991617],"study_design_scores_gemma":[0.0000620975,0.0002276407,0.0005257509,0.00001421813,0.00001380915,0.0001034996,0.0000158674,0.9529453,0.04016363,0.002093865,0.003817048,0.0000172662],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05590371,0.0003533198,0.9403833,0.0001212838,0.0001688411,0.0000407451,0.00002686243,0.0008041286,0.002197865],"genre_scores_gemma":[0.8609545,0.0002297304,0.1361254,0.0001169176,0.00006330957,0.00007200227,0.00007016271,0.00004091278,0.002327079],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001581554,"threshold_uncertainty_score":0.005290806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.123884355351038,"score_gpt":0.2548479010018987,"score_spread":0.1309635456508607,"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."}}