{"id":"W2926228387","doi":"10.1109/access.2019.2907126","title":"Disturbance Observer Based on Biologically Inspired Integral Sliding Mode Control for Trajectory Tracking of Mobile Robots","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Control and Dynamics of Mobile Robots","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"National Natural Science Foundation of China","keywords":"Control theory (sociology); Trajectory; Kinematics; Robustness (evolution); Mobile robot; Computer science; Feed forward; Backstepping; Nonlinear system; Integral sliding mode; Lyapunov function; Sliding mode control; Control engineering; Robot; Engineering; Adaptive control; Artificial intelligence; Control (management); 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":[0.000278526,0.0003715843,0.0003819438,0.0002949909,0.0002413087,0.0004868943,0.0005992999,0.0004861854,0.0006792694],"category_scores_gemma":[0.0006287954,0.000159004,0.0003515936,0.000213358,0.0004228026,0.0004828509,0.0003916404,0.0005846563,0.0001362014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004171006,"about_ca_system_score_gemma":0.0004831826,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003265678,"about_ca_topic_score_gemma":0.001936893,"domain_scores_codex":[0.9998031,0.00002506513,0.00001446111,0.00004520277,0.00009324602,0.00001903432],"domain_scores_gemma":[0.9998399,0.00004172661,0.00002558597,0.00001632835,0.000067601,0.000008788932],"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":[0.0002751869,0.0001076604,0.001971553,0.00054346,0.0001115954,0.0003891612,0.000555344,0.6279385,0.1060057,0.04406048,0.001841513,0.2161998],"study_design_scores_gemma":[0.00001742191,0.0001014444,0.0003238019,0.00001222271,0.00001487446,0.00004185979,0.00001498457,0.989852,0.006018287,0.001330021,0.002261507,0.00001150282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01074828,0.0003167614,0.9862152,0.00006187699,0.00009307122,0.0000202316,0.000009590988,0.0003243496,0.002210574],"genre_scores_gemma":[0.9172903,0.0006735837,0.07717849,0.00008479899,0.00005190257,0.0001262163,0.00007750228,0.00002972767,0.004487394],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003265678,"threshold_uncertainty_score":0.00649333,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01974481737673034,"score_gpt":0.264332144373172,"score_spread":0.2445873269964417,"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."}}