{"id":"W4390588181","doi":"10.1007/s11071-023-09205-z","title":"Filtered low-power multi-high-gain observer design for a class of nonlinear systems","year":2024,"lang":"en","type":"article","venue":"Nonlinear Dynamics","topic":"Adaptive Control of Nonlinear Systems","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Differentiator; Control theory (sociology); Observer (physics); Alpha beta filter; State observer; Nonlinear system; Mathematics; Separation principle; Convergence (economics); Computer science; Control (management); Statistics; Kalman filter; Filter (signal processing); Extended Kalman filter; Artificial intelligence; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004650882,0.0006900833,0.0006555101,0.0002611694,0.0002929379,0.0007028258,0.0006471333,0.0008675539,0.001603069],"category_scores_gemma":[0.0009053611,0.0002273969,0.0003351615,0.0001994973,0.0004312083,0.0006845027,0.0004773048,0.0007036387,0.0004036656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003851325,"about_ca_system_score_gemma":0.00049054,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002475492,"about_ca_topic_score_gemma":0.003848991,"domain_scores_codex":[0.9998106,0.00003045646,0.00001432875,0.00005052756,0.00006939104,0.00002479036],"domain_scores_gemma":[0.9996603,0.0001003166,0.00004769064,0.00003790975,0.0001401631,0.0000137017],"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":[0.0008726519,0.0002370968,0.001985605,0.0008827607,0.0001815788,0.0004521856,0.0005215739,0.526951,0.1429402,0.03226539,0.004575837,0.2881341],"study_design_scores_gemma":[0.00002132864,0.00008600142,0.0003830804,0.000008787625,0.00001114655,0.00003027595,0.000009052786,0.9936329,0.003596085,0.001329317,0.0008833873,0.000008713288],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01645394,0.0002043783,0.981114,0.00008287509,0.00004760922,0.00002889136,0.00002360262,0.0001500029,0.001894586],"genre_scores_gemma":[0.9253898,0.0003306521,0.06963372,0.00008667701,0.00005661062,0.0001216105,0.0001104304,0.00002663176,0.004243791],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002475492,"threshold_uncertainty_score":0.005362809,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02661062446301673,"score_gpt":0.2484449471487864,"score_spread":0.2218343226857696,"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."}}