{"id":"W3003557230","doi":"10.1109/globalsip45357.2019.8969275","title":"Multi-Objective Gain Optimizer for an Active Disturbance Rejection Controller","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Active disturbance rejection control; Control theory (sociology); Disturbance (geology); Sorting; Inverted pendulum; Computer science; Genetic algorithm; Controller (irrigation); Control engineering; Control (management); Engineering; Nonlinear system; Algorithm; Artificial intelligence; 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":[0.0007989697,0.001038355,0.0006191407,0.0006484801,0.0003333194,0.0008049664,0.0007019998,0.0007658515,0.002799746],"category_scores_gemma":[0.00109116,0.0003811591,0.0004731743,0.0003173363,0.0003523128,0.0003030196,0.0005548719,0.0007528293,0.0005591537],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006778993,"about_ca_system_score_gemma":0.0007191765,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002809074,"about_ca_topic_score_gemma":0.003217734,"domain_scores_codex":[0.9997087,0.00007446944,0.00001116334,0.00003862373,0.0001331197,0.00003385602],"domain_scores_gemma":[0.999781,0.00008769923,0.00004003551,0.00001383255,0.00006562089,0.00001180956],"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":[0.00006488715,0.00005502592,0.0002332201,0.00006900605,0.00003757401,0.00004536468,0.00003465873,0.9500023,0.00500966,0.003611235,0.0005490208,0.04028801],"study_design_scores_gemma":[0.000009934858,0.00004685666,0.00009939933,0.000004897737,0.000005739943,0.000008028302,0.000003844908,0.9986798,0.0005328752,0.0002914385,0.0003145623,0.000002557788],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02896954,0.0002082903,0.9630892,0.00009661275,0.00002812296,0.00008685563,0.00003249807,0.0006785854,0.006810313],"genre_scores_gemma":[0.7357097,0.0001434412,0.2578506,0.00008785746,0.00002687819,0.0003450088,0.00009725343,0.0001160649,0.005623248],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002809074,"threshold_uncertainty_score":0.009366095,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00952092470983748,"score_gpt":0.2362363885627222,"score_spread":0.2267154638528847,"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."}}