{"id":"W2769118354","doi":"10.1002/rnc.3995","title":"H<sub>∞</sub>model predictive control for constrained discrete‐time piecewise affine systems","year":2017,"lang":"en","type":"article","venue":"International Journal of Robust and Nonlinear Control","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Control theory (sociology); Model predictive control; Mathematics; Mathematical optimization; Lyapunov function; Piecewise; Norm (philosophy); Discrete time and continuous time; Minification; Quadratic equation; Infimum and supremum; Controller (irrigation); Linear matrix inequality; Stability theory; Computer science; Nonlinear system; Control (management)","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":[0.000424262,0.0002398729,0.0005383866,0.0001639312,0.0001435453,0.0002750617,0.0004336843,0.0001245146,0.000003640637],"category_scores_gemma":[0.0004203276,0.0002127041,0.0001770223,0.00002088014,0.00009812009,0.0007052221,0.00002211364,0.0002031106,0.000003272822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001156642,"about_ca_system_score_gemma":0.00007359559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002531934,"about_ca_topic_score_gemma":0.000004161223,"domain_scores_codex":[0.9984958,0.00003290032,0.0007000203,0.0001660976,0.000364039,0.000241123],"domain_scores_gemma":[0.997797,0.000241452,0.0006305578,0.000190665,0.000987038,0.0001533577],"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":[0.0008308687,0.00002911927,0.0001973416,0.00002472713,0.0007627804,0.00002198518,0.0000395188,0.9681793,0.02656222,0.0002167864,0.0002030985,0.002932242],"study_design_scores_gemma":[0.0113461,0.0001525327,0.0001771182,0.0001747608,0.0001611951,0.0001209019,0.00002550929,0.9867835,0.0004273589,0.0001151246,0.0003214625,0.0001944043],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01869065,0.0007068823,0.9768754,0.0006333635,0.001399447,0.00061742,0.0007042232,0.00005905432,0.0003135891],"genre_scores_gemma":[0.993892,0.0001240703,0.004091359,0.00004804859,0.001679811,0.0000372387,0.00002011918,0.00004818938,0.00005919901],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9752013,"threshold_uncertainty_score":0.8673822,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007764170730829412,"score_gpt":0.227385746861834,"score_spread":0.2196215761310046,"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."}}