{"id":"W3034170716","doi":"10.3390/aerospace7060071","title":"Pareto Optimal PID Tuning for Px4-Based Unmanned Aerial Vehicles by Using a Multi-Objective Particle Swarm Optimization Algorithm","year":2020,"lang":"en","type":"article","venue":"Aerospace","topic":"Adaptive Control of Nonlinear Systems","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Consejo Nacional de Ciencia y Tecnología, Paraguay","keywords":"PID controller; Particle swarm optimization; Overshoot (microwave communication); Control theory (sociology); Pareto principle; Computer science; Multi-objective optimization; Pareto optimal; Process (computing); Control engineering; Mathematical optimization; Engineering; Algorithm; Mathematics; Control (management); Temperature control; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006964998,0.0008387395,0.00048761,0.0004275057,0.0003703862,0.0005813667,0.0005051687,0.0005285459,0.001073468],"category_scores_gemma":[0.0009955202,0.0002949999,0.0004674038,0.0002072292,0.0003540026,0.0002528852,0.0004954608,0.0005349733,0.0002226472],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003487889,"about_ca_system_score_gemma":0.0007206358,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003768789,"about_ca_topic_score_gemma":0.002429104,"domain_scores_codex":[0.9998047,0.00004780554,0.00001156856,0.00003611898,0.0000741353,0.00002567319],"domain_scores_gemma":[0.9997252,0.0001111977,0.00005644736,0.00001779854,0.00007602468,0.00001327143],"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":[0.00005701684,0.00004239808,0.0004838313,0.0000872591,0.00003652991,0.00006612,0.00007198962,0.9464195,0.007632794,0.002436935,0.0003917486,0.04227388],"study_design_scores_gemma":[0.000007942651,0.00004272658,0.0001250449,0.000004097059,0.000004026729,0.000007739039,0.000005990992,0.9983437,0.0008308152,0.0003001091,0.0003248299,0.000002878421],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0271629,0.0001209249,0.9685205,0.00004238559,0.00003109887,0.00007422122,0.0000142811,0.0002376087,0.003796055],"genre_scores_gemma":[0.7693599,0.0001529939,0.2270366,0.00005368825,0.00002227421,0.0002529302,0.00006180919,0.00004980642,0.00300991],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003768789,"threshold_uncertainty_score":0.007493734,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03007917611414871,"score_gpt":0.2495585609795093,"score_spread":0.2194793848653606,"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."}}