{"id":"W2138268561","doi":"10.23919/acc.2004.1384672","title":"Piecewise-affine state feedback using convex optimization","year":2004,"lang":"en","type":"article","venue":"","topic":"Stability and Control of Uncertain Systems","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Parameterized complexity; Control theory (sociology); Piecewise; Lyapunov function; Mathematics; Affine transformation; Controller (irrigation); Convex optimization; Regular polygon; State (computer science); State vector; Quadratic equation; Optimization problem; Convex function; Mathematical optimization; Computer science; Control (management); Nonlinear system; Algorithm; Mathematical analysis; Artificial intelligence; Pure 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008562933,0.0001108526,0.0001560456,0.00004156355,0.00003910653,0.0000431977,0.00007408064,0.00004560156,0.0002248885],"category_scores_gemma":[0.0000121287,0.0001064776,0.00004022129,0.0001333079,0.00002002954,0.0001572996,0.000009216298,0.00005947782,0.00004548513],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001401902,"about_ca_system_score_gemma":0.00002246419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000161669,"about_ca_topic_score_gemma":0.00004651714,"domain_scores_codex":[0.9993652,0.00000964704,0.0002078279,0.0001154287,0.0001110427,0.0001908823],"domain_scores_gemma":[0.9997041,0.00001922024,0.00001801249,0.0001577623,0.00004333249,0.00005756609],"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":[0.00000609178,0.000009503755,0.00009040511,0.00002453265,0.00002022342,0.000001596083,0.0001391339,0.9966522,0.001891035,0.0002599968,0.00003155231,0.0008737607],"study_design_scores_gemma":[0.001235701,0.00002336789,0.0001769086,0.00002974139,0.00001204546,0.000006965375,0.0001241743,0.994806,0.002060469,0.0004567034,0.0008556822,0.0002122226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1362389,0.0002244291,0.8524662,0.0001052873,0.0003838132,0.000210738,0.000004905958,0.0004712617,0.009894446],"genre_scores_gemma":[0.9874339,0.0000147667,0.01219572,0.00006086644,0.00007652216,0.000006095927,0.000004667615,0.00002285708,0.0001846834],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8511949,"threshold_uncertainty_score":0.434203,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01172784745628342,"score_gpt":0.1980786590289288,"score_spread":0.1863508115726454,"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."}}