{"id":"W2903617107","doi":"10.1109/tase.2018.2883343","title":"Adaptive Control and Optimization of Mobile Manipulation Subject to Input Saturation and Switching Constraints","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Automation Science and Engineering","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Control theory (sociology); Holonomic; Nonholonomic system; Actuator; Optimal control; Control engineering; Motion control; Computer science; Adaptive control; Holonomic constraints; Mobile robot; Engineering; Robot; Control (management); Mathematical optimization; Mathematics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002611692,0.0006480042,0.0004570579,0.0002293164,0.0002222245,0.0004025986,0.0004447206,0.000413877,0.00123838],"category_scores_gemma":[0.00048177,0.0002221631,0.0003218793,0.0002941478,0.0005119284,0.0003326537,0.0005164118,0.0004712779,0.000129573],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004173414,"about_ca_system_score_gemma":0.0004663101,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004133996,"about_ca_topic_score_gemma":0.003041296,"domain_scores_codex":[0.9998477,0.00003155667,0.000006537812,0.00003816872,0.0000513135,0.00002463157],"domain_scores_gemma":[0.9998542,0.00007127789,0.00003149143,0.000008029767,0.00002828325,0.000006732214],"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":[0.00006213026,0.00002966336,0.0002012013,0.0001157833,0.00003047725,0.00009032136,0.00006471051,0.9359337,0.01133793,0.01533004,0.0003917474,0.03641233],"study_design_scores_gemma":[0.000004417293,0.0000253076,0.00007276655,0.00000213003,0.000002294572,0.000005337976,0.000003482822,0.9981813,0.0004489601,0.001027862,0.0002242755,0.000001817491],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03865984,0.0005093659,0.9551946,0.0001111829,0.00004386373,0.00003056594,0.0000231035,0.0001734414,0.005254085],"genre_scores_gemma":[0.969443,0.0002855477,0.02643341,0.00004489681,0.00003494933,0.0001164578,0.000038114,0.00002044396,0.003583157],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004133996,"threshold_uncertainty_score":0.008219898,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01199919089421735,"score_gpt":0.2288543348527369,"score_spread":0.2168551439585195,"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."}}