{"id":"W2153148809","doi":"10.1002/rnc.1829","title":"Distributed consensus control for multi‐agent systems using terminal sliding mode and Chebyshev neural networks","year":2011,"lang":"en","type":"article","venue":"International Journal of Robust and Nonlinear Control","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":100,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Control theory (sociology); Terminal sliding mode; Artificial neural network; Controller (irrigation); Computer science; Sliding mode control; Nonlinear system; Tracking error; Lyapunov function; Bounded function; Mathematics; Control (management); 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007835864,0.0002667985,0.000555525,0.0001932971,0.0001347402,0.0003692578,0.0006678345,0.0001236007,0.000002003283],"category_scores_gemma":[0.0002201495,0.0002285715,0.0001852869,0.00008087568,0.0000703329,0.0004095274,0.00008280062,0.0002421595,5.005252e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001075586,"about_ca_system_score_gemma":0.00008267053,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008289018,"about_ca_topic_score_gemma":0.000006885932,"domain_scores_codex":[0.9978308,0.0001651358,0.0009221454,0.0003119721,0.0004051048,0.0003648341],"domain_scores_gemma":[0.9973621,0.0003263247,0.0008481328,0.0001786395,0.001031224,0.000253548],"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":[0.007477721,0.001550993,0.06489162,0.0002409303,0.006424477,0.003079999,0.001772772,0.8592138,0.01198602,0.01214713,0.0006340834,0.03058051],"study_design_scores_gemma":[0.0100942,0.0001683538,0.001472827,0.0001213314,0.0001321404,0.001456216,0.00008992277,0.9859892,0.00003955923,0.00002473198,0.0001927151,0.0002188644],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08061372,0.001176771,0.9149182,0.0003059478,0.002184579,0.0004569154,0.0003077796,0.00002984114,0.000006194683],"genre_scores_gemma":[0.9771093,0.00002177125,0.02191456,0.0001371475,0.0007612371,0.00001262826,0.00001120634,0.00001834956,0.00001383773],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8964955,"threshold_uncertainty_score":0.9320876,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06533249496723814,"score_gpt":0.290897782951506,"score_spread":0.2255652879842679,"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."}}