Globally Stable Adaptive Backstepping Neural Network Control for Uncertain Strict-Feedback Systems With Tracking Accuracy Known a Priori
This paper addresses the problem of globally stable direct adaptive backstepping neural network (NN) tracking control design for a class of uncertain strict-feedback systems under the assumption that the accuracy of the ultimate tracking error is given a priori. In contrast to the classical adaptive...
Saved in:
| Published in | IEEE transaction on neural networks and learning systems Vol. 26; no. 9; pp. 1842 - 1854 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
IEEE
01.09.2015
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | This paper addresses the problem of globally stable direct adaptive backstepping neural network (NN) tracking control design for a class of uncertain strict-feedback systems under the assumption that the accuracy of the ultimate tracking error is given a priori. In contrast to the classical adaptive backstepping NN control schemes, this paper analyzes the convergence of the tracking error using Barbalat's Lemma via some nonnegative functions rather than the positive-definite Lyapunov functions. Thus, the accuracy of the ultimate tracking error can be determined and adjusted accurately a priori, and the closed-loop system is guaranteed to be globally uniformly ultimately bounded. The main technical novelty is to construct three new n th-order continuously differentiable functions, which are used to design the control law, the virtual control variables, and the adaptive laws. Finally, two simulation examples are given to illustrate the effectiveness and advantages of the proposed control method. |
|---|---|
| AbstractList | This paper addresses the problem of globally stable direct adaptive backstepping neural network (NN) tracking control design for a class of uncertain strict-feedback systems under the assumption that the accuracy of the ultimate tracking error is given a priori . In contrast to the classical adaptive backstepping NN control schemes, this paper analyzes the convergence of the tracking error using Barbalat's Lemma via some nonnegative functions rather than the positive-definite Lyapunov functions. Thus, the accuracy of the ultimate tracking error can be determined and adjusted accurately a priori , and the closed-loop system is guaranteed to be globally uniformly ultimately bounded. The main technical novelty is to construct three new [Formula Omitted]th-order continuously differentiable functions, which are used to design the control law, the virtual control variables, and the adaptive laws. Finally, two simulation examples are given to illustrate the effectiveness and advantages of the proposed control method. This paper addresses the problem of globally stable direct adaptive backstepping neural network (NN) tracking control design for a class of uncertain strict-feedback systems under the assumption that the accuracy of the ultimate tracking error is given a priori. In contrast to the classical adaptive backstepping NN control schemes, this paper analyzes the convergence of the tracking error using Barbalat's Lemma via some nonnegative functions rather than the positive-definite Lyapunov functions. Thus, the accuracy of the ultimate tracking error can be determined and adjusted accurately a priori, and the closed-loop system is guaranteed to be globally uniformly ultimately bounded. The main technical novelty is to construct three new n th-order continuously differentiable functions, which are used to design the control law, the virtual control variables, and the adaptive laws. Finally, two simulation examples are given to illustrate the effectiveness and advantages of the proposed control method. This paper addresses the problem of globally stable direct adaptive backstepping neural network (NN) tracking control design for a class of uncertain strict-feedback systems under the assumption that the accuracy of the ultimate tracking error is given a priori. In contrast to the classical adaptive backstepping NN control schemes, this paper analyzes the convergence of the tracking error using Barbalat's Lemma via some nonnegative functions rather than the positive-definite Lyapunov functions. Thus, the accuracy of the ultimate tracking error can be determined and adjusted accurately a priori, and the closed-loop system is guaranteed to be globally uniformly ultimately bounded. The main technical novelty is to construct three new n th-order continuously differentiable functions, which are used to design the control law, the virtual control variables, and the adaptive laws. Finally, two simulation examples are given to illustrate the effectiveness and advantages of the proposed control method.This paper addresses the problem of globally stable direct adaptive backstepping neural network (NN) tracking control design for a class of uncertain strict-feedback systems under the assumption that the accuracy of the ultimate tracking error is given a priori. In contrast to the classical adaptive backstepping NN control schemes, this paper analyzes the convergence of the tracking error using Barbalat's Lemma via some nonnegative functions rather than the positive-definite Lyapunov functions. Thus, the accuracy of the ultimate tracking error can be determined and adjusted accurately a priori, and the closed-loop system is guaranteed to be globally uniformly ultimately bounded. The main technical novelty is to construct three new n th-order continuously differentiable functions, which are used to design the control law, the virtual control variables, and the adaptive laws. Finally, two simulation examples are given to illustrate the effectiveness and advantages of the proposed control method. |
| Author | Jian Wu Maoguo Gong Ge, Shuzhi Sam Weisheng Chen |
| Author_xml | – sequence: 1 surname: Weisheng Chen fullname: Weisheng Chen email: wshchen@126.com organization: Sch. of Math. & Stat., Xidian Univ., Xi'an, China – sequence: 2 givenname: Shuzhi Sam surname: Ge fullname: Ge, Shuzhi Sam email: samge@nus.edu.sg organization: Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore, Singapore, Singapore – sequence: 3 surname: Jian Wu fullname: Jian Wu email: jwu2011@126.com organization: Sch. of Math. & Stat., Xidian Univ., Xi'an, China – sequence: 4 surname: Maoguo Gong fullname: Maoguo Gong email: gong@ieee.org organization: Key Lab. of Intell. Perception & Image Understanding, Xidian Univ., Xi'an, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/25265634$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kd9uFCEYxYmpsbX2BTQxJN54MyswMH8u142txs1qstvoHWGYb5SWhRFYm30DH1vG3faiF3LBR8jvnMA5z9GJ8w4QeknJjFLSvtusVsv1jBHKZ6wUNRf0CTpjtGIFK5vm5OFcfz9FFzHekLwqIirePkOnTLBKVCU_Q3-urO-UtXu8TqqzgOe9GpP5Dfi90rcxwTga9wOvYBeUzSPd-XCLF96l4C0efMDXTkNIyrjsEIxOxSVA32UxXu-zfhvxN5N-4k3IV5PVXOvspff4s_N3Div8NRgfzAv0dFA2wsVxnqPryw-bxcdi-eXq02K-LDRnNBVCk0YLqjvd0prmH-lqADJ0bUmqgTHa17zsOxC01mzoGq6bvJetaATnrOeqPEdvD75j8L92EJPcmqjBWuXA76KkNakYEXVLM_rmEXrjd8Hl101UzrzldKJeH6ldt4VejsFsVdjL-4wz0BwAHXyMAQapTVLJTBkqYyUlcmpU_mtUTo3KY6NZyh5J793_K3p1EBkAeBBULSWsacu_2VCsSA |
| CODEN | ITNNAL |
| CitedBy_id | crossref_primary_10_1155_2019_9053858 crossref_primary_10_1016_j_neucom_2017_10_042 crossref_primary_10_1080_00207721_2020_1817616 crossref_primary_10_1109_TNNLS_2023_3264151 crossref_primary_10_1109_ACCESS_2017_2788446 crossref_primary_10_1007_s11071_019_05321_x crossref_primary_10_1109_TASE_2024_3374522 crossref_primary_10_1049_iet_cta_2016_0034 crossref_primary_10_1109_ACCESS_2020_3004401 crossref_primary_10_1109_ACCESS_2019_2919249 crossref_primary_10_1109_TSMC_2023_3281982 crossref_primary_10_1016_j_neucom_2021_01_017 crossref_primary_10_1016_j_ins_2016_10_016 crossref_primary_10_1109_TNNLS_2020_2967150 crossref_primary_10_1109_TFUZZ_2017_2762285 crossref_primary_10_1109_TSMC_2016_2597305 crossref_primary_10_1016_j_isatra_2015_09_018 crossref_primary_10_1109_ACCESS_2019_2929841 crossref_primary_10_1109_TFUZZ_2019_2945487 crossref_primary_10_1109_TSMC_2017_2709813 crossref_primary_10_1002_rnc_7720 crossref_primary_10_1007_s00521_016_2576_1 crossref_primary_10_1080_00207179_2020_1857845 crossref_primary_10_1109_TSMC_2018_2854770 crossref_primary_10_1016_j_neucom_2021_11_097 crossref_primary_10_1177_0142331220985637 crossref_primary_10_1016_j_ins_2018_03_057 crossref_primary_10_1016_j_robot_2019_02_017 crossref_primary_10_1109_TNNLS_2016_2547968 crossref_primary_10_1016_j_neucom_2019_12_102 crossref_primary_10_1115_1_4044372 crossref_primary_10_1109_TSMC_2021_3094975 crossref_primary_10_1007_s11071_021_06237_1 crossref_primary_10_1002_mma_7832 crossref_primary_10_1155_2019_9765353 crossref_primary_10_1016_j_amc_2021_126665 crossref_primary_10_1177_0142331216658508 crossref_primary_10_1109_TNNLS_2017_2650238 crossref_primary_10_1016_j_neucom_2024_128034 crossref_primary_10_1016_j_ins_2018_06_042 crossref_primary_10_1007_s40815_023_01518_w crossref_primary_10_1109_TFUZZ_2019_2892920 crossref_primary_10_1109_TNNLS_2019_2934403 crossref_primary_10_1007_s00521_021_06387_8 crossref_primary_10_1109_TNNLS_2021_3120620 crossref_primary_10_1155_2019_8602719 crossref_primary_10_1109_TCYB_2016_2607166 crossref_primary_10_1109_TCYB_2022_3158702 crossref_primary_10_1109_TIE_2024_3436632 crossref_primary_10_1007_s00202_021_01353_y crossref_primary_10_1016_j_neucom_2024_127869 crossref_primary_10_1109_TCYB_2019_2931877 crossref_primary_10_1109_TFUZZ_2023_3348271 crossref_primary_10_1016_j_neucom_2020_10_034 crossref_primary_10_1109_TCYB_2016_2581173 crossref_primary_10_1109_TNNLS_2018_2844165 crossref_primary_10_1016_j_cnsns_2024_108222 crossref_primary_10_1109_TNNLS_2022_3155635 crossref_primary_10_1049_cth2_12517 crossref_primary_10_1016_j_jfranklin_2021_12_004 crossref_primary_10_1016_j_amc_2021_126579 crossref_primary_10_1080_00207721_2017_1282062 crossref_primary_10_1109_JSYST_2024_3445377 crossref_primary_10_1109_TCYB_2020_3012607 crossref_primary_10_1109_TCSII_2023_3310275 crossref_primary_10_1109_TNNLS_2015_2499757 crossref_primary_10_1016_j_isatra_2023_01_032 crossref_primary_10_1002_rnc_7586 crossref_primary_10_1016_j_ifacol_2020_12_2567 crossref_primary_10_1016_j_isatra_2023_11_007 crossref_primary_10_1109_TNNLS_2019_2919676 crossref_primary_10_1002_rnc_4352 crossref_primary_10_1016_j_neucom_2015_09_013 crossref_primary_10_1080_00207721_2016_1151962 crossref_primary_10_1016_j_ins_2017_03_013 crossref_primary_10_1109_TCYB_2022_3178385 crossref_primary_10_1109_TCYB_2020_3030310 crossref_primary_10_1109_TFUZZ_2020_3022570 crossref_primary_10_1109_TNNLS_2017_2765683 crossref_primary_10_1109_TSMC_2021_3099808 crossref_primary_10_1080_00207721_2020_1793232 crossref_primary_10_1109_TCYB_2020_2965800 crossref_primary_10_1109_TFUZZ_2016_2593500 crossref_primary_10_1007_s11071_015_2396_3 crossref_primary_10_1109_TNNLS_2021_3072552 crossref_primary_10_1109_TNNLS_2017_2766123 crossref_primary_10_1016_j_ins_2020_03_015 crossref_primary_10_1002_asjc_1646 crossref_primary_10_1109_TNNLS_2017_2651903 crossref_primary_10_1109_TCNS_2024_3425665 crossref_primary_10_1109_TCYB_2021_3056990 crossref_primary_10_1109_TAI_2021_3093499 crossref_primary_10_1109_TSMC_2018_2800783 crossref_primary_10_1002_oca_3251 crossref_primary_10_1002_acs_3165 crossref_primary_10_1016_j_neucom_2019_10_006 crossref_primary_10_1016_j_isatra_2020_08_036 crossref_primary_10_1016_j_engappai_2023_107533 crossref_primary_10_1016_j_amc_2024_129110 crossref_primary_10_1007_s40815_018_0515_2 crossref_primary_10_1049_iet_cta_2016_0471 crossref_primary_10_1109_TCYB_2022_3192356 crossref_primary_10_1109_TFUZZ_2019_2905827 crossref_primary_10_1109_TNNLS_2021_3054611 crossref_primary_10_1109_TSMC_2023_3245299 crossref_primary_10_1109_TCYB_2018_2875134 crossref_primary_10_1016_j_ins_2017_08_085 crossref_primary_10_1109_TSMC_2022_3143359 crossref_primary_10_1109_TNNLS_2020_3027335 crossref_primary_10_1016_j_jfranklin_2015_11_006 crossref_primary_10_1016_j_neunet_2017_09_013 crossref_primary_10_1109_ACCESS_2019_2920716 crossref_primary_10_1109_TCYB_2017_2655053 crossref_primary_10_1109_TSMC_2015_2470635 crossref_primary_10_1177_1729881419894065 crossref_primary_10_1002_asjc_2720 crossref_primary_10_1007_s11431_022_2140_9 crossref_primary_10_1109_TSMC_2018_2870642 crossref_primary_10_1007_s11071_020_05599_2 crossref_primary_10_1017_S0263574718000723 crossref_primary_10_1109_JSYST_2023_3274160 crossref_primary_10_1515_ijnsns_2020_0141 crossref_primary_10_1016_j_nahs_2017_01_005 crossref_primary_10_1007_s40815_018_0545_9 crossref_primary_10_1016_j_comcom_2020_04_046 crossref_primary_10_1016_j_cnsns_2023_107506 crossref_primary_10_1109_TMECH_2018_2808235 crossref_primary_10_1109_TSMC_2016_2558098 crossref_primary_10_1016_j_asr_2022_08_042 crossref_primary_10_1109_ACCESS_2016_2591978 crossref_primary_10_1109_TIE_2016_2595481 crossref_primary_10_1016_j_jfranklin_2018_11_023 crossref_primary_10_1016_j_isatra_2022_06_013 crossref_primary_10_1109_TMECH_2024_3377679 crossref_primary_10_1109_TASE_2023_3243405 crossref_primary_10_1137_23M1595035 crossref_primary_10_1016_j_jfranklin_2022_11_038 crossref_primary_10_1007_s40815_021_01085_y crossref_primary_10_1109_TCYB_2017_2655501 crossref_primary_10_1109_TNNLS_2016_2628038 crossref_primary_10_1109_TSMC_2019_2954875 crossref_primary_10_1109_TNNLS_2017_2655503 crossref_primary_10_1109_ACCESS_2019_2891689 crossref_primary_10_1109_TNNLS_2017_2716947 crossref_primary_10_1186_s13662_019_2099_z crossref_primary_10_1016_j_ast_2022_107752 crossref_primary_10_1016_j_amc_2019_06_008 crossref_primary_10_1109_TNNLS_2018_2793968 crossref_primary_10_1016_j_neucom_2017_06_042 crossref_primary_10_1109_TCYB_2017_2758385 crossref_primary_10_1007_s11071_020_05885_z crossref_primary_10_1109_TSMC_2020_2994808 crossref_primary_10_1007_s11071_019_05018_1 crossref_primary_10_1109_TCYB_2023_3336992 crossref_primary_10_1109_TSMC_2022_3218399 crossref_primary_10_1109_TNNLS_2017_2712619 crossref_primary_10_1155_2021_3396482 crossref_primary_10_1002_asjc_3116 crossref_primary_10_1007_s12555_021_0433_1 crossref_primary_10_1109_TNNLS_2021_3131364 crossref_primary_10_20965_jrm_2022_p0615 crossref_primary_10_1002_asjc_2253 crossref_primary_10_1177_01423312241267048 crossref_primary_10_1186_s13662_018_1642_7 crossref_primary_10_1002_acs_2823 crossref_primary_10_1016_j_ins_2018_10_032 crossref_primary_10_1080_00207721_2024_2304133 crossref_primary_10_1109_TNNLS_2021_3051030 crossref_primary_10_1002_asjc_3344 crossref_primary_10_1109_ACCESS_2020_3036161 crossref_primary_10_3233_JIFS_190540 crossref_primary_10_1016_j_isatra_2020_08_027 crossref_primary_10_1016_j_isatra_2021_08_048 crossref_primary_10_1109_TFUZZ_2020_2966185 crossref_primary_10_1109_TCYB_2015_2411285 crossref_primary_10_1109_TIE_2021_3123641 crossref_primary_10_1155_2019_6920372 crossref_primary_10_1016_j_ast_2019_03_058 crossref_primary_10_1109_TSMC_2016_2627048 crossref_primary_10_1142_S1469026817500195 crossref_primary_10_1155_2019_5613212 |
| Cites_doi | 10.1109/TNN.2004.839354 10.1016/j.neucom.2013.05.042 10.1016/j.automatica.2008.08.012 10.1016/S0005-1098(02)00067-5 10.1109/TNNLS.2012.2213305 10.1109/TNN.2011.2165222 10.1002/0471221139 10.1109/TNN.2010.2047115 10.1109/TNN.2007.912318 10.1016/j.automatica.2009.02.012 10.1109/TSMCB.2008.918568 10.1109/TSMCB.2009.2033563 10.1109/TNNLS.2011.2178448 10.1109/9.486648 10.1109/TNN.2011.2146274 10.1109/TNN.2006.888368 10.1016/j.automatica.2009.02.021 10.1007/978-1-4471-0549-7 10.1109/TNN.2011.2140381 10.1109/TNN.2004.826215 10.1016/j.automatica.2008.11.017 10.1109/TNN.2009.2022159 10.1109/TNN.2011.2159865 10.1049/iet-cta.2009.0428 10.1109/72.950139 10.23919/ACC.1991.4791778 10.1002/asjc.160 10.1109/TSMCB.2003.817055 10.1109/TNN.2005.844907 10.1109/9.827369 10.1016/S0005-1098(00)00116-3 10.1109/TSMCB.2009.2016110 10.1109/72.870049 10.1109/TSMCB.2009.2033808 10.1049/iet-cta.2009.0341 10.1109/3468.895898 10.1162/neco.1991.3.2.246 10.1016/j.sysconle.2008.09.002 10.1016/S0005-1098(02)00034-1 10.1109/TSMCB.2004.833340 10.1109/9.100933 10.1109/TNN.2009.2038999 10.1049/iet-cta.2009.0478 10.1109/9.481548 10.1109/TNN.2008.2001266 10.1080/002071798222280 10.1109/TSMCB.2007.904544 |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2015 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2015 |
| DBID | 97E RIA RIE AAYXX CITATION CGR CUY CVF ECM EIF NPM 7QF 7QO 7QP 7QQ 7QR 7SC 7SE 7SP 7SR 7TA 7TB 7TK 7U5 8BQ 8FD F28 FR3 H8D JG9 JQ2 KR7 L7M L~C L~D P64 7X8 |
| DOI | 10.1109/TNNLS.2014.2357451 |
| DatabaseName | IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE/IET Electronic Library CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Aluminium Industry Abstracts Biotechnology Research Abstracts Calcium & Calcified Tissue Abstracts Ceramic Abstracts Chemoreception Abstracts Computer and Information Systems Abstracts Corrosion Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts Materials Business File Mechanical & Transportation Engineering Abstracts Neurosciences Abstracts Solid State and Superconductivity Abstracts METADEX Technology Research Database ANTE: Abstracts in New Technology & Engineering Engineering Research Database Aerospace Database Materials Research Database ProQuest Computer Science Collection Civil Engineering Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Biotechnology and BioEngineering Abstracts MEDLINE - Academic |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Materials Research Database Technology Research Database Computer and Information Systems Abstracts – Academic Mechanical & Transportation Engineering Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Materials Business File Aerospace Database Engineered Materials Abstracts Biotechnology Research Abstracts Chemoreception Abstracts Advanced Technologies Database with Aerospace ANTE: Abstracts in New Technology & Engineering Civil Engineering Abstracts Aluminium Industry Abstracts Electronics & Communications Abstracts Ceramic Abstracts Neurosciences Abstracts METADEX Biotechnology and BioEngineering Abstracts Computer and Information Systems Abstracts Professional Solid State and Superconductivity Abstracts Engineering Research Database Calcium & Calcified Tissue Abstracts Corrosion Abstracts MEDLINE - Academic |
| DatabaseTitleList | Materials Research Database MEDLINE - Academic MEDLINE |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database – sequence: 3 dbid: RIE name: IEEE/IET Electronic Library url: https://proxy.k.utb.cz/login?url=https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 2162-2388 |
| EndPage | 1854 |
| ExternalDocumentID | 3784212001 25265634 10_1109_TNNLS_2014_2357451 6910289 |
| Genre | orig-research Research Support, Non-U.S. Gov't Journal Article |
| GrantInformation_xml | – fundername: Basic Research Program of China (973 Program) grantid: 2011CB707005 – fundername: National Natural Science Foundation of China grantid: 61174213; 61203074 funderid: 10.13039/501100001809 – fundername: Program for New Century Excellent Talents in University grantid: NCET-10-0665 funderid: 10.13039/501100004602 |
| GroupedDBID | 0R~ 4.4 5VS 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACIWK ACPRK AENEX AFRAH AGQYO AGSQL AHBIQ AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ EBS EJD IFIPE IPLJI JAVBF M43 MS~ O9- OCL PQQKQ RIA RIE RNS AAYXX CITATION RIG CGR CUY CVF ECM EIF NPM 7QF 7QO 7QP 7QQ 7QR 7SC 7SE 7SP 7SR 7TA 7TB 7TK 7U5 8BQ 8FD F28 FR3 H8D JG9 JQ2 KR7 L7M L~C L~D P64 7X8 |
| ID | FETCH-LOGICAL-c421t-5c08c51cbc9171060c6fe0fb9306f221d743dbe517c2fb84c8fb839585442d4a3 |
| IEDL.DBID | RIE |
| ISSN | 2162-237X 2162-2388 |
| IngestDate | Thu Jul 10 23:20:37 EDT 2025 Mon Jun 30 06:48:32 EDT 2025 Thu Jan 02 22:55:36 EST 2025 Tue Jul 01 00:27:18 EDT 2025 Thu Apr 24 23:05:31 EDT 2025 Tue Aug 26 16:37:37 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | true |
| Issue | 9 |
| Keywords | tracking accuracy known a priori Adaptive backstepping design uncertain strict-feedback system Barbalat’s Lemma radial basis function (RBF) neural network (NN) |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c421t-5c08c51cbc9171060c6fe0fb9306f221d743dbe517c2fb84c8fb839585442d4a3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| PMID | 25265634 |
| PQID | 1705749411 |
| PQPubID | 85436 |
| PageCount | 13 |
| ParticipantIDs | ieee_primary_6910289 crossref_primary_10_1109_TNNLS_2014_2357451 crossref_citationtrail_10_1109_TNNLS_2014_2357451 proquest_journals_1705749411 proquest_miscellaneous_1706205791 pubmed_primary_25265634 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2015-09-01 |
| PublicationDateYYYYMMDD | 2015-09-01 |
| PublicationDate_xml | – month: 09 year: 2015 text: 2015-09-01 day: 01 |
| PublicationDecade | 2010 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States – name: Piscataway |
| PublicationTitle | IEEE transaction on neural networks and learning systems |
| PublicationTitleAbbrev | TNNLS |
| PublicationTitleAlternate | IEEE Trans Neural Netw Learn Syst |
| PublicationYear | 2015 |
| Publisher | IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher_xml | – name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| References | ref13 ref15 ref52 ref11 ref10 krstic (ref5) 1995 ref16 ref19 yoo (ref40) 2008; 19 ref18 isidori (ref1) 1999 hua (ref30) 2008; 19 liu (ref17) 2011; 22 ref51 ref50 slotine (ref45) 1991 wen (ref27) 2011; 22 ref46 li (ref12) 2010; 40 ref42 ref41 ref43 ref49 ref8 ref7 ref9 ref4 ref6 ren (ref44) 2010; 21 ref34 ref36 ref31 ref33 sanner (ref47) 1991 yoo (ref32) 2009; 39 ref2 ref39 ref38 khalil (ref3) 2006 chen (ref35) 2009; 45 ref24 ref23 chen (ref37) 2010; 12 ref25 ref20 ref22 ref21 yoo (ref26) 2009; 20 ref28 ref29 du (ref14) 2008; 44 spong (ref48) 1989 |
| References_xml | – ident: ref10 doi: 10.1109/TNN.2004.839354 – ident: ref46 doi: 10.1016/j.neucom.2013.05.042 – ident: ref36 doi: 10.1016/j.automatica.2008.08.012 – year: 2006 ident: ref3 publication-title: Nonlinear Systems – ident: ref16 doi: 10.1016/S0005-1098(02)00067-5 – ident: ref38 doi: 10.1109/TNNLS.2012.2213305 – volume: 22 start-page: 1599 year: 2011 ident: ref27 article-title: Neural networks-based adaptive control for nonlinear time-varying delays systems with unknown control direction publication-title: IEEE Trans Neural Netw doi: 10.1109/TNN.2011.2165222 – year: 1989 ident: ref48 publication-title: Robot Dynamics and Control – ident: ref2 doi: 10.1002/0471221139 – volume: 21 start-page: 1339 year: 2010 ident: ref44 article-title: Adaptive neural control for output feedback nonlinear systems using a barrier Lyapunov function publication-title: IEEE Trans Neural Netw doi: 10.1109/TNN.2010.2047115 – year: 1991 ident: ref45 publication-title: Applied nonlinear control – volume: 19 start-page: 673 year: 2008 ident: ref30 article-title: Output feedback stabilization for time-delay nonlinear interconnected systems using neural networks publication-title: IEEE Trans Neural Netw doi: 10.1109/TNN.2007.912318 – ident: ref22 doi: 10.1016/j.automatica.2009.02.012 – ident: ref25 doi: 10.1109/TSMCB.2008.918568 – volume: 40 start-page: 915 year: 2010 ident: ref12 article-title: A DSC approach to robust adaptive NN tracking control for strict-feedback nonlinear systems publication-title: IEEE Trans Syst Man Cybern B Cybern doi: 10.1109/TSMCB.2009.2033563 – ident: ref33 doi: 10.1109/TNNLS.2011.2178448 – ident: ref6 doi: 10.1109/9.486648 – ident: ref31 doi: 10.1109/TNN.2011.2146274 – ident: ref24 doi: 10.1109/TNN.2006.888368 – volume: 45 start-page: 1554 year: 2009 ident: ref35 article-title: Novel adaptive neural control design for nonlinear MIMO time-delay systems publication-title: Automatica doi: 10.1016/j.automatica.2009.02.021 – year: 1999 ident: ref1 publication-title: Nonlinear Control Systems II doi: 10.1007/978-1-4471-0549-7 – ident: ref50 doi: 10.1109/TNN.2011.2140381 – ident: ref9 doi: 10.1109/TNN.2004.826215 – ident: ref43 doi: 10.1016/j.automatica.2008.11.017 – volume: 20 start-page: 1209 year: 2009 ident: ref26 article-title: Adaptive neural control for a class of strict-feedback nonlinear systems with state time delays publication-title: IEEE Trans Neural Netw doi: 10.1109/TNN.2009.2022159 – volume: 22 start-page: 1328 year: 2011 ident: ref17 article-title: Adaptive neural output feedback controller design with reduced-order observer for a class of uncertain nonlinear SISO systems publication-title: IEEE Trans Neural Netw doi: 10.1109/TNN.2011.2159865 – ident: ref20 doi: 10.1049/iet-cta.2009.0428 – ident: ref15 doi: 10.1109/72.950139 – start-page: 2153 year: 1991 ident: ref47 article-title: gaussian networks for direct adaptive control publication-title: 1991 American Control Conference ACC doi: 10.23919/ACC.1991.4791778 – volume: 12 start-page: 96 year: 2010 ident: ref37 article-title: Globally decentralized adaptive backstepping neural network tracking control for unknown nonlinear interconnected systems publication-title: Asian Journal of Contro doi: 10.1002/asjc.160 – ident: ref23 doi: 10.1109/TSMCB.2003.817055 – ident: ref28 doi: 10.1109/TNN.2005.844907 – ident: ref52 doi: 10.1109/9.827369 – ident: ref8 doi: 10.1016/S0005-1098(00)00116-3 – year: 1995 ident: ref5 publication-title: Nonlinear and Adaptive Control Design – volume: 39 start-page: 1316 year: 2009 ident: ref32 article-title: Neural-network-based decentralized adaptive control for a class of large-scale nonlinear systems with unknown time-varying delays publication-title: IEEE Trans Syst Man Cybern B Cybern doi: 10.1109/TSMCB.2009.2016110 – ident: ref39 doi: 10.1109/72.870049 – ident: ref19 doi: 10.1109/TSMCB.2009.2033808 – ident: ref42 doi: 10.1049/iet-cta.2009.0341 – ident: ref34 doi: 10.1109/3468.895898 – ident: ref49 doi: 10.1162/neco.1991.3.2.246 – ident: ref21 doi: 10.1016/j.sysconle.2008.09.002 – ident: ref13 doi: 10.1016/S0005-1098(02)00034-1 – ident: ref18 doi: 10.1109/TSMCB.2004.833340 – ident: ref4 doi: 10.1109/9.100933 – ident: ref11 doi: 10.1109/TNN.2009.2038999 – volume: 44 start-page: 1895 year: 2008 ident: ref14 article-title: Adaptive neural network control for a class of low-triangular-structured nonlinear systems publication-title: Automatica – ident: ref41 doi: 10.1049/iet-cta.2009.0478 – ident: ref51 doi: 10.1109/9.481548 – volume: 19 start-page: 1712 year: 2008 ident: ref40 article-title: Adaptive output feedback control of flexible-joint robots using neural networks: Dynamic surface design approach publication-title: IEEE Trans Neural Netw doi: 10.1109/TNN.2008.2001266 – ident: ref7 doi: 10.1080/002071798222280 – ident: ref29 doi: 10.1109/TSMCB.2007.904544 |
| SSID | ssj0000605649 |
| Score | 2.562895 |
| Snippet | This paper addresses the problem of globally stable direct adaptive backstepping neural network (NN) tracking control design for a class of uncertain... |
| SourceID | proquest pubmed crossref ieee |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 1842 |
| SubjectTerms | Accuracy Adaptive backstepping design Adaptive systems Algorithms Approximation methods Artificial neural networks Backstepping Barbalat's Lemma Computer Simulation Control systems Feedback Humans Lyapunov methods Neural networks Neural Networks, Computer Nonlinear Dynamics radial basis function (RBF) neural network (NN) tracking accuracy known a priori Tracking stock uncertain strict-feedback system Uncertainty |
| Title | Globally Stable Adaptive Backstepping Neural Network Control for Uncertain Strict-Feedback Systems With Tracking Accuracy Known a Priori |
| URI | https://ieeexplore.ieee.org/document/6910289 https://www.ncbi.nlm.nih.gov/pubmed/25265634 https://www.proquest.com/docview/1705749411 https://www.proquest.com/docview/1706205791 |
| Volume | 26 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3db9QwDI-2PfHCgPFxMFCQeIPckjRNm8dj4jQhdkJiJ-6tatyUnTj1pqP3MP4C_mzs9EMCAeKlqlTHTWWnthP7Z8Zeld7lUKtSYIzshcmkF95ZJbIcrHQedJZT7fDlwl4szftVujpgb8ZamBBCTD4LU7qNZ_nVFva0VXZmHZlDd8gOMXDrarXG_RSJfrmN3q5WVgudZKuhRka6s6vF4sMnSuQyU8J3MSl1iNEEDW8T84tJij1W_u5uRrMzP2aXw4S7bJOv033rp_D9NyzH__2ie-xu73_yWacw99lBaB6w46G3A--X-gn70TUD2NxydEf9JvBZVd7Qr5G_par8NhCuwxdO2B7IbtElk_PzLvGdoyfMl8gnphsgB_zZtmKOhtLjYN6jpPPP6_aao7EE2q7nMwDkBbecOnE3vOQfd-vtbv2QLefvrs4vRN-1QYDRqhUpyBxSBR4wEsSAU4Ktg6y9w-Ck1lpV6LNUPqQqA1373ECO18Rh2GKMrkyZPGJHzbYJTxhPy6SCpLJ1haJVVe1rn0qocwA6IZLlhKlBcAX0kObUWWNTxNBGuiLKvSC5F73cJ-z1OOamA_T4J_UJCW2k7OU1YaeDfhT9mv9WEDBRZpxROOrl-BhXKx3BlE3Y7iON1VT_izSPO70aeQ_q-PTP73zG7uDM0i6_7ZQdtbt9eI4OUetfxJXwE3JhBbI |
| linkProvider | IEEE |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3dj9MwDI-O4wFeOOD4GBwQJN6guyZN0-ZxnJgGbBUSm9hb1bgpTEzdaXQPx1_An42dfkggQLxUleq4qezUdmL_zNiLwpoUKlEEGCPbQCWhDazRIkhS0KGxIJOUaocXmZ6t1Lt1vD5ir4ZaGOecTz5zY7r1Z_nlDg60VXauDZlDc41dR7sfy7Zaa9hRCdEz197flULLQEbJuq-SCc35MsvmHymVS40J4UXF1CNGEji8jtQvRsl3Wfm7w-kNz_SELfopt_kmX8eHxo7h-29ojv_7TbfZrc4D5ZNWZe6wI1ffZSd9dwfeLfZT9qNtB7C94uiQ2q3jk7K4pJ8jf011-Y0jZIfPnNA9kF3WppPzizb1naMvzFfIxyccIAf83TbBFE2lxcG8w0nnnzbNF47mEmjDnk8AkBdccerFXfOCf9hvdvvNPbaavllezIKub0MASoomiCFMIRZgAWNBDDlD0JULK2swPKmkFCV6LaV1sUhAVjZVkOI1Mhi4KCVLVUT32XG9q91DxuMiKiEqdVWiaEVZ2crGIVQpAJ0RhcWIiV5wOXSg5tRbY5v74CY0uZd7TnLPO7mP2MthzGUL6fFP6lMS2kDZyWvEznr9yLtV_y0naKJEGSVw1PPhMa5XOoQparc7eBotqQIYaR60ejXw7tXx0Z_f-YzdmC0X83z-Nnv_mN3EWcZtttsZO272B_cE3aPGPvWr4ic74gj8 |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Globally+Stable+Adaptive+Backstepping+Neural+Network+Control+for+Uncertain+Strict-Feedback+Systems+With+Tracking+Accuracy+Known+a+Priori&rft.jtitle=IEEE+transaction+on+neural+networks+and+learning+systems&rft.au=Chen%2C+Weisheng&rft.au=Ge%2C+Shuzhi+Sam&rft.au=Wu%2C+Jian&rft.au=Gong%2C+Maoguo&rft.date=2015-09-01&rft.issn=2162-237X&rft.eissn=2162-2388&rft.volume=26&rft.issue=9&rft.spage=1842&rft.epage=1854&rft_id=info:doi/10.1109%2FTNNLS.2014.2357451&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_TNNLS_2014_2357451 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2162-237X&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2162-237X&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2162-237X&client=summon |