{"id":"W4245610470","doi":"10.32920/14638164","title":"Comparative Study of DE, PSO and GA for Position Domain PID Controller Tuning","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Iterative Learning Control Systems","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"PID controller; Particle swarm optimization; Control theory (sociology); Position (finance); Controller (irrigation); Differential evolution; Computer science; Nonlinear system; Tracking (education); Premature convergence; Convergence (economics); Genetic algorithm; Domain (mathematical analysis); Evolutionary algorithm; Mathematics; Artificial intelligence; Mathematical optimization; Control engineering; Engineering; Algorithm; Control (management)","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":[],"consensus_categories":[],"category_scores_codex":[0.0003732979,0.0002456565,0.0008213061,0.0001093397,0.00005477154,0.0001134298,0.00009005614,0.000145319,0.00001496244],"category_scores_gemma":[0.00002400752,0.0002395222,0.00008364223,0.00005191428,0.00001979307,0.00005616418,0.00008335761,0.0003207129,8.301045e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001001429,"about_ca_system_score_gemma":0.00002537241,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008038031,"about_ca_topic_score_gemma":0.0001036884,"domain_scores_codex":[0.9987707,0.0002397902,0.0003975912,0.0002725832,0.000129338,0.000189976],"domain_scores_gemma":[0.999215,0.0002622623,0.0001171368,0.0001786937,0.0001797515,0.00004717322],"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":[0.0004465563,0.0004908658,0.007597648,0.002114274,0.004349631,0.00002837001,0.1520696,0.6533504,0.1760775,0.001458798,0.001207607,0.0008088217],"study_design_scores_gemma":[0.009170325,0.0006770216,0.01223075,0.0007372992,0.0002547396,0.00001362914,0.02438464,0.949396,0.001688918,0.0003789665,0.0003401692,0.0007275926],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7726588,0.0007311084,0.2228997,0.0000212288,0.0002031973,0.001592113,0.00001499165,0.0001054482,0.001773384],"genre_scores_gemma":[0.9958357,0.000003736758,0.003461457,0.00001540299,0.0001134823,0.0003452481,0.00003468043,0.00003281147,0.0001575133],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2960455,"threshold_uncertainty_score":0.9767432,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01776129648849483,"score_gpt":0.2723872553771101,"score_spread":0.2546259588886153,"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."}}