{"id":"W4400123103","doi":"10.1080/00207721.2024.2367711","title":"A robust interpolated model predictive control based on recurrent neural networks for a nonholonomic differential-drive mobile robot with quasi-LPV representation: computational complexity and conservatism","year":2024,"lang":"en","type":"article","venue":"International Journal of Systems Science","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Representation (politics); Computer science; Mobile robot; Control theory (sociology); Nonholonomic system; Differential (mechanical device); Control (management); Artificial neural network; Model predictive control; Artificial intelligence; Robot; Control engineering; Engineering","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":[0.000340766,0.0001793835,0.0002936159,0.0003617414,0.00009083769,0.0003574506,0.0003115342,0.00004680047,0.000002273025],"category_scores_gemma":[0.00005119051,0.0001477999,0.00006773905,0.0002110443,0.00022984,0.0006806697,0.00002053845,0.0002074152,5.467269e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004009482,"about_ca_system_score_gemma":0.0001327526,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001117188,"about_ca_topic_score_gemma":0.000004483958,"domain_scores_codex":[0.9983241,0.00005013546,0.0005982974,0.0002709889,0.0005697839,0.0001866968],"domain_scores_gemma":[0.9982756,0.0003709734,0.000271789,0.0001000824,0.0008705882,0.000111002],"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":[0.0003392132,0.00003277639,0.000323146,0.00003357153,0.0001261749,0.00001273124,0.0001933642,0.9972302,0.0003120431,0.0007524702,0.00005677896,0.0005874983],"study_design_scores_gemma":[0.001650152,0.000411133,0.0007035601,0.000606042,0.00003594513,0.0001502911,0.00008727708,0.9960744,0.00001229765,0.0001260374,0.000007019999,0.0001358176],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04019835,0.0002905986,0.9561552,0.0001616439,0.002324765,0.0006594462,0.000111685,0.00006997025,0.00002829162],"genre_scores_gemma":[0.9936356,0.000008739665,0.005890166,0.00003494809,0.0002856806,0.00009510109,0.00001976932,0.00002339409,0.000006625693],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9534372,"threshold_uncertainty_score":0.6027107,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02433568957260327,"score_gpt":0.2692562338806161,"score_spread":0.2449205443080129,"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."}}