{"id":"W2129600290","doi":"10.1109/pesc.2004.1355085","title":"Adaptive backstepping based nonlinear control of an IPMSM drive","year":2004,"lang":"en","type":"article","venue":"2004 IEEE 35th Annual Power Electronics Specialists Conference (IEEE Cat. No.04CH37551)","topic":"Sensorless Control of Electric Motors","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Backstepping; Control theory (sociology); Controller (irrigation); Lyapunov stability; Nonlinear system; MATLAB; Computer science; Electronic speed control; Control engineering; Lyapunov function; Adaptive control; Stability (learning theory); Scheme (mathematics); Engineering; Control (management); Mathematics","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":[0.0002492302,0.000310216,0.0001853858,0.0001089858,0.000129787,0.0002704065,0.0003516685,0.0002697293,0.0008221913],"category_scores_gemma":[0.0005628887,0.00007855226,0.0001215415,0.00007952833,0.000274276,0.0001835499,0.0001791846,0.0002929108,0.0001096189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001745555,"about_ca_system_score_gemma":0.0001894484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00137711,"about_ca_topic_score_gemma":0.001330773,"domain_scores_codex":[0.99989,0.0000238067,0.000006259964,0.00001847695,0.00005400816,0.000007380241],"domain_scores_gemma":[0.999849,0.00005938331,0.00002746733,0.000012852,0.00004400331,0.000007355928],"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":[0.0007046025,0.0001899282,0.00137503,0.0005080638,0.00007030916,0.0004467288,0.0003646993,0.4703017,0.2530603,0.008419716,0.001055379,0.2635036],"study_design_scores_gemma":[0.00002759094,0.0004173992,0.0005953676,0.000006626588,0.00001001582,0.00005162764,0.000009523714,0.9852855,0.01212727,0.000386922,0.001074992,0.00000727878],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2348353,0.0003863422,0.7575725,0.0001243634,0.0001220537,0.00007612152,0.00002431172,0.0005523814,0.00630659],"genre_scores_gemma":[0.9784888,0.00008630093,0.01980696,0.00001926738,0.00001335946,0.00003255862,0.00001290292,0.00000552937,0.001534258],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00137711,"threshold_uncertainty_score":0.002750516,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008800282594586331,"score_gpt":0.2200050253183577,"score_spread":0.2112047427237714,"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."}}