{"id":"W3199285136","doi":"10.18280/jesa.540407","title":"Intelligent Control of Uncertain PMSM Based on Stable and Adaptive Discrete-Time Neural Network Compensators","year":2021,"lang":"en","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Sensorless Control of Electric Motors","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Control theory (sociology); Artificial neural network; Controller (irrigation); Lyapunov function; Lyapunov stability; Stability (learning theory); Adaptive control; Torque; Engineering; Control engineering; Vector control; Electronic speed control; Computer science; Control (management); Induction motor; Voltage; Physics; Nonlinear system; Artificial intelligence","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":[0.000301572,0.0004592328,0.0003178753,0.0001629626,0.0002589253,0.0003420821,0.000493379,0.0004178089,0.0004909667],"category_scores_gemma":[0.0005205378,0.0001640748,0.0002681286,0.0001600913,0.0003133517,0.0003893408,0.0002524456,0.0003832847,0.00007134604],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002627784,"about_ca_system_score_gemma":0.000279481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001993209,"about_ca_topic_score_gemma":0.002260681,"domain_scores_codex":[0.9998298,0.00002945069,0.00001296778,0.00003886064,0.00007118919,0.00001769633],"domain_scores_gemma":[0.9998578,0.00004548148,0.00003850243,0.00001129551,0.00004036543,0.000006457656],"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":[0.0002531126,0.00005513336,0.0006767173,0.0002840849,0.00006655739,0.0002481977,0.0001711698,0.8107119,0.04900564,0.01157433,0.0009020037,0.1260511],"study_design_scores_gemma":[0.00001829487,0.00008089662,0.0003276588,0.000005519251,0.000009965283,0.00002672153,0.000005246629,0.995374,0.002597448,0.0008174801,0.0007302389,0.0000065892],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05954774,0.0007389914,0.9355409,0.0001448483,0.000136415,0.00004285595,0.000025738,0.0004211835,0.003401274],"genre_scores_gemma":[0.9753885,0.0002214728,0.02290371,0.00003946269,0.00002930083,0.00005347277,0.00002359657,0.00001005434,0.001330514],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001993209,"threshold_uncertainty_score":0.003963172,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01269894155097594,"score_gpt":0.2194476018566699,"score_spread":0.2067486603056939,"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."}}