{"id":"W2154217080","doi":"10.1109/ias.1988.25112","title":"Synthesis of optimal sliding mode control for robust DC drive","year":2003,"lang":"en","type":"article","venue":"Conference Record of the 1988 IEEE Industry Applications Society Annual Meeting","topic":"Iterative Learning Control Systems","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Weighting; Control theory (sociology); Computation; Optimal control; Riccati equation; Matrix (chemical analysis); Minification; Computer science; Quadratic equation; Mathematical optimization; Selection (genetic algorithm); Algebraic Riccati equation; Optimal design; Mathematics; Algorithm; Control (management); Artificial intelligence","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":[0.0003621512,0.000417244,0.0003458167,0.0003166838,0.0002325493,0.0005993979,0.0003417453,0.0004500939,0.002823531],"category_scores_gemma":[0.0006082565,0.0002342854,0.0002678439,0.0001753338,0.0003097748,0.0002262091,0.0002913403,0.0003664332,0.000348513],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003728664,"about_ca_system_score_gemma":0.0006270912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001019693,"about_ca_topic_score_gemma":0.001154426,"domain_scores_codex":[0.999877,0.00001427048,0.000008393282,0.00002665782,0.00005997719,0.00001373666],"domain_scores_gemma":[0.999877,0.00003333687,0.00002424904,0.00001475378,0.00004317165,0.000007547563],"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":[0.000131983,0.00008597828,0.0003498666,0.0004253298,0.00004348139,0.0001797998,0.0001994871,0.586594,0.07177195,0.1019841,0.003243683,0.2349904],"study_design_scores_gemma":[0.0000213215,0.00009439229,0.0001347169,0.00001878828,0.000007402632,0.00002164665,0.000009973697,0.9831249,0.007717433,0.004608329,0.004232246,0.000008957203],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0108282,0.0001612879,0.9809175,0.0001109966,0.0000628573,0.00007354841,0.00003755701,0.0003890915,0.00741903],"genre_scores_gemma":[0.7403285,0.0002843669,0.2532215,0.00007993187,0.00003739424,0.0002845455,0.0001073609,0.00006465948,0.005591675],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002823531,"threshold_uncertainty_score":0.009445667,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02036245210967581,"score_gpt":0.2474609166294353,"score_spread":0.2270984645197595,"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."}}