{"id":"W3134163494","doi":"10.1109/lcsys.2021.3086682","title":"Low-Gain Stability of Projected Integral Control for Input-Constrained Discrete-Time Nonlinear Systems","year":2021,"lang":"en","type":"preprint","venue":"IEEE Control Systems Letters","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Control theory (sociology); Monotone polygon; Nonlinear system; Discrete time and continuous time; Mathematics; Stability (learning theory); Exponential stability; Controller (irrigation); Constant (computer programming); Integral sliding mode; Regular polygon; Control (management); Computer science; Sliding mode control; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.001564974,0.001411521,0.003983608,0.0004736587,0.0001216408,0.0004457482,0.0009116158,0.001062995,0.000009170482],"category_scores_gemma":[0.000547006,0.001417954,0.0009709071,0.0003706877,0.0002423698,0.0003823065,0.00007762603,0.001123004,0.000009892666],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001071627,"about_ca_system_score_gemma":0.0003588184,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004945729,"about_ca_topic_score_gemma":0.00003515965,"domain_scores_codex":[0.9922191,0.001004923,0.003313548,0.001390166,0.0008460488,0.001226196],"domain_scores_gemma":[0.9942033,0.001159231,0.001418157,0.001819937,0.001137314,0.0002620344],"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":[0.0002627222,0.00006286834,0.00009979914,0.005444047,0.001435399,0.00001942418,0.0002811002,0.7491199,0.2428997,0.00002304035,0.0002294657,0.0001224798],"study_design_scores_gemma":[0.008386828,0.0001013782,0.0000197203,0.002653855,0.0005194352,0.00002556118,0.0003399994,0.9846718,0.001907929,0.000003715023,0.0001606065,0.001209212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08101625,0.002367681,0.8893266,0.0002257911,0.008243914,0.01344041,0.004216195,0.00106839,0.00009482267],"genre_scores_gemma":[0.991349,0.00001342604,0.001207214,0.0001101115,0.001388757,0.004764724,0.0007671675,0.0003591478,0.00004046216],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9103327,"threshold_uncertainty_score":0.9998635,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008444511552020197,"score_gpt":0.215838984322815,"score_spread":0.2073944727707948,"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."}}