{"id":"W2406405348","doi":"10.1007/s11071-016-2828-8","title":"Non-smooth predictive control for mechanical transmission systems with backlash-like hysteresis","year":2016,"lang":"en","type":"article","venue":"Nonlinear Dynamics","topic":"Iterative Learning Control Systems","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"National Natural Science Foundation of China","keywords":"Backlash; Control theory (sociology); Model predictive control; Hysteresis; Mechanical system; Transmission (telecommunications); Function (biology); Transmission system; Stability (learning theory); Work (physics); Series (stratigraphy); Control system; Engineering; Computer science; Control engineering; Control (management); Physics; Artificial intelligence; Mechanical engineering","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":[0.0002730526,0.0003332866,0.0005094048,0.00009869636,0.00008874713,0.00006948893,0.0002180627,0.0002246369,0.0000092452],"category_scores_gemma":[0.00002178445,0.0002120139,0.0001220105,0.0001172289,0.00004195169,0.0001829698,0.0000104344,0.0001805306,0.0000346118],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003016384,"about_ca_system_score_gemma":0.00004515059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009429199,"about_ca_topic_score_gemma":0.00004126722,"domain_scores_codex":[0.9984154,0.00007007112,0.0004062116,0.0003584893,0.0002844251,0.0004654049],"domain_scores_gemma":[0.9989034,0.0003273493,0.00008911124,0.0003281245,0.0001917444,0.00016029],"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":[0.009247621,0.0008111745,0.01623331,0.005756586,0.005293252,0.0001795454,0.00269687,0.6617607,0.1597022,0.006888995,0.005075743,0.126354],"study_design_scores_gemma":[0.004338979,0.0004845458,0.0002752263,0.0005262739,0.00008027216,0.00001220531,0.00006891457,0.9860886,0.0001451113,0.00001302615,0.007642277,0.0003245655],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02889474,0.0001035774,0.967611,0.000142327,0.000614999,0.001362446,0.0004065134,0.0003976393,0.0004668206],"genre_scores_gemma":[0.9934905,0.000009822631,0.004350148,0.00002800761,0.0004126363,0.000241655,0.00004983255,0.0001489615,0.001268423],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9645958,"threshold_uncertainty_score":0.8645675,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004151356195057829,"score_gpt":0.19568895049371,"score_spread":0.1915375942986522,"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."}}