{"id":"W4297792233","doi":"10.1016/j.ifacol.2022.07.605","title":"GWO-Based Optimal Tuning of Controllers for Shape Memory Alloy Wire Actuators","year":2022,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Shape Memory Alloy Transformations","field":"Materials Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Education and Research, Romania; Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii; Corporation for National and Community Service","keywords":"Control theory (sociology); SMA*; Controller (irrigation); Actuator; Fuzzy logic; Nonlinear system; Computer science; Shape-memory alloy; Fuzzy control system; Control engineering; Engineering; Algorithm; Control (management); Artificial intelligence","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008566591,0.0002604527,0.0004575987,0.0001795041,0.0005382469,0.00003773442,0.000566541,0.00006939937,0.005069362],"category_scores_gemma":[0.0001689403,0.0002611792,0.0002579563,0.0002787098,0.0001607779,0.0002721836,0.00007678019,0.0001992543,0.00003011101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001367651,"about_ca_system_score_gemma":0.0003036429,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003074669,"about_ca_topic_score_gemma":0.00002231288,"domain_scores_codex":[0.9978647,0.0001390142,0.000563361,0.0003919391,0.000558219,0.0004828302],"domain_scores_gemma":[0.998675,0.0004507056,0.000254926,0.000355175,0.0001244734,0.0001396583],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007893036,0.0003107629,0.00009072105,0.0001188542,0.00005846195,0.0000117722,0.002023172,0.1046823,0.8882484,0.0001852531,0.00006907199,0.003411904],"study_design_scores_gemma":[0.009570089,0.00108858,0.0002773842,0.00006178704,0.0002353062,0.00002770797,0.008050284,0.8476008,0.1286394,0.00003260778,0.003593307,0.000822842],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9900489,0.00008781868,0.00482469,0.001799734,0.000600408,0.0009279283,0.001044636,0.0001889623,0.0004769244],"genre_scores_gemma":[0.8458511,0.000002444221,0.1523357,0.0009020257,0.0001067064,0.0002868394,0.0001910543,0.00004905738,0.0002750903],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.759609,"threshold_uncertainty_score":0.999984,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01957580449935913,"score_gpt":0.2578551046227437,"score_spread":0.2382793001233846,"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."}}