{"id":"W4376607912","doi":"10.1109/tie.2023.3274869","title":"Policy Learning for Nonlinear Model Predictive Control With Application to USVs","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Electronics","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"National Natural Science Foundation of China","keywords":"Model predictive control; Nonlinear system; Control theory (sociology); Computer science; Artificial neural network; Reinforcement learning; Computation; Motion control; Artificial intelligence; Control (management); Robot; Control engineering; Mathematical optimization; Machine learning; Engineering; Mathematics; Algorithm","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":[0.0006853081,0.0006638036,0.0006747973,0.0002549714,0.000407169,0.0006397594,0.0005610971,0.0007425992,0.001397966],"category_scores_gemma":[0.002010658,0.0003086045,0.000295969,0.0004267955,0.0007510741,0.0004456432,0.0007960909,0.00121112,0.0001928311],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008299901,"about_ca_system_score_gemma":0.001317999,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01124143,"about_ca_topic_score_gemma":0.005888869,"domain_scores_codex":[0.9997268,0.00007871926,0.00001618674,0.0000559827,0.00008940083,0.00003281657],"domain_scores_gemma":[0.9994658,0.0002918863,0.00006033446,0.00003248201,0.0001294444,0.00002015543],"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":[0.00003272476,0.00001732739,0.0001501153,0.00006388891,0.000009526409,0.00003487601,0.00003187494,0.9635769,0.0007907268,0.006030441,0.0005490782,0.02871252],"study_design_scores_gemma":[0.000002594248,0.000007855888,0.0000177249,0.000002220922,8.791571e-7,0.000002387004,0.000001870364,0.9984786,0.0001583614,0.001099993,0.0002261503,0.000001304341],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01057541,0.0007875287,0.9842557,0.0003622431,0.0001061956,0.00003817219,0.0000269992,0.0003229537,0.003524827],"genre_scores_gemma":[0.9314974,0.0008524531,0.06372125,0.0001238465,0.00009288696,0.000140361,0.00006579314,0.00005014872,0.003455843],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01124143,"threshold_uncertainty_score":0.02235198,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01198478039370388,"score_gpt":0.2418821685521476,"score_spread":0.2298973881584437,"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."}}