{"id":"W4318984782","doi":"10.25046/aj080106","title":"Nonlinear Model Predictive Control of Rover Robotics System","year":2023,"lang":"en","type":"article","venue":"Advances in Science Technology and Engineering Systems Journal","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Robotics; Nonlinear model; Artificial intelligence; Nonlinear system; Computer science; Model predictive control; Control engineering; Control (management); Engineering; Robot; Physics","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":[0.000356699,0.000790201,0.0007764727,0.0002677729,0.0004069828,0.001030108,0.0008315852,0.0005694667,0.00129201],"category_scores_gemma":[0.0005694439,0.0002602264,0.0003524971,0.0003261235,0.0004838016,0.000389931,0.0006141707,0.0008032687,0.0003223314],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000529449,"about_ca_system_score_gemma":0.0007551623,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009862448,"about_ca_topic_score_gemma":0.005971218,"domain_scores_codex":[0.9997457,0.00005087875,0.00001295875,0.00007356695,0.00008331149,0.00003358412],"domain_scores_gemma":[0.999803,0.00005843422,0.00003907712,0.00001902366,0.00007370614,0.000006813353],"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":[0.00003472786,0.00001496717,0.0002398632,0.0001293416,0.00001835175,0.00007225535,0.00004579839,0.961741,0.002393971,0.004706557,0.0006799438,0.02992328],"study_design_scores_gemma":[0.000006406002,0.00002152425,0.0001133541,0.000005696954,0.000004524516,0.00001063337,0.000004883549,0.9976327,0.0004463629,0.0009088037,0.0008412389,0.000003855847],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01686661,0.0009847333,0.9712356,0.000227096,0.0001199114,0.00006349031,0.00008372285,0.0006475277,0.009771252],"genre_scores_gemma":[0.9419531,0.001012491,0.04961358,0.0001064039,0.00008998915,0.0002845064,0.0002135727,0.00003798712,0.006688517],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009862448,"threshold_uncertainty_score":0.01961011,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003997892361729784,"score_gpt":0.2154433421386773,"score_spread":0.2114454497769475,"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."}}