{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001131384,0.0006026148,0.0006788973,0.0009074589,0.0002658455,0.0008676515,0.0004674528,0.0007453834,0.0006522223],"category_scores_gemma":[0.003413679,0.0001868656,0.0003845262,0.0006937925,0.0002896539,0.0005997928,0.0003268967,0.0005010501,0.0001112001],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003811028,"about_ca_system_score_gemma":0.0004741903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003242166,"about_ca_topic_score_gemma":0.002735713,"domain_scores_codex":[0.9995178,0.0001086223,0.00004090655,0.00007545164,0.0002117204,0.00004558806],"domain_scores_gemma":[0.9989935,0.0006055758,0.00008212542,0.00008156796,0.0002153021,0.00002187163],"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":[0.0003298137,0.0001611774,0.003655214,0.0004274524,0.0001743385,0.0001656363,0.0001235903,0.6786365,0.01313425,0.008008391,0.0008252748,0.2943584],"study_design_scores_gemma":[0.00002495077,0.0001702909,0.001759665,0.00002133074,0.00003720271,0.00009349702,0.00003819246,0.9890665,0.006322821,0.0008347759,0.001615522,0.00001539179],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3193341,0.008321723,0.6453907,0.0004026596,0.0001758917,0.0001588119,0.00009717617,0.0009997287,0.02511919],"genre_scores_gemma":[0.8700134,0.002180523,0.1247407,0.00007969009,0.00003423181,0.0001144895,0.0001069518,0.00006727879,0.00266285],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003242166,"threshold_uncertainty_score":0.0064466,"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."}}