Robust adaptive neural network control of a class of uncertain strict-feedback nonlinear systems with unknown dead-zone and disturbances
In this paper, a robust adaptive neural control design approach is presented for a class of perturbed strict-feedback nonlinear systems with unknown dead-zone. In the controller design, different from existing methods, all the virtual control laws need not be actually implemented at intermediate ste...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Sun, Gang [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2014transfer abstract |
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| Schlagwörter: |
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| Umfang: |
9 |
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| Übergeordnetes Werk: |
Enthalten in: The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast - Liu, Yang ELSEVIER, 2018, an international journal, Amsterdam |
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| Übergeordnetes Werk: |
volume:145 ; year:2014 ; day:5 ; month:12 ; pages:221-229 ; extent:9 |
| Links: |
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| DOI / URN: |
10.1016/j.neucom.2014.05.039 |
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ELV017635500 |
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| 520 | |a In this paper, a robust adaptive neural control design approach is presented for a class of perturbed strict-feedback nonlinear systems with unknown dead-zone. In the controller design, different from existing methods, all the virtual control laws need not be actually implemented at intermediate steps, and only one actual robust adaptive control law is constructed by approximating the lumped unknown function of the system with a single neural network at the last step. By this approach, the structure of the designed controller is much simpler since the causes for the problem of complexity growing in existing methods are eliminated. Stability analysis shows that the proposed scheme can guarantee the uniform ultimate boundedness of all the closed-loop system signals, and the steady-state tracking error can be made arbitrarily small by appropriately choosing control parameters. Simulation studies demonstrate the effectiveness and merits of the proposed approach. | ||
| 520 | |a In this paper, a robust adaptive neural control design approach is presented for a class of perturbed strict-feedback nonlinear systems with unknown dead-zone. In the controller design, different from existing methods, all the virtual control laws need not be actually implemented at intermediate steps, and only one actual robust adaptive control law is constructed by approximating the lumped unknown function of the system with a single neural network at the last step. By this approach, the structure of the designed controller is much simpler since the causes for the problem of complexity growing in existing methods are eliminated. Stability analysis shows that the proposed scheme can guarantee the uniform ultimate boundedness of all the closed-loop system signals, and the steady-state tracking error can be made arbitrarily small by appropriately choosing control parameters. Simulation studies demonstrate the effectiveness and merits of the proposed approach. | ||
| 650 | 7 | |a Single neural network |2 Elsevier | |
| 650 | 7 | |a Uncertain strict-feedback nonlinear systems |2 Elsevier | |
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| 700 | 1 | |a Wang, Mingxin |4 oth | |
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10.1016/j.neucom.2014.05.039 doi GBVA2014014000023.pica (DE-627)ELV017635500 (ELSEVIER)S0925-2312(14)00686-9 DE-627 ger DE-627 rakwb eng 610 610 DE-600 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Sun, Gang verfasserin aut Robust adaptive neural network control of a class of uncertain strict-feedback nonlinear systems with unknown dead-zone and disturbances 2014transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, a robust adaptive neural control design approach is presented for a class of perturbed strict-feedback nonlinear systems with unknown dead-zone. In the controller design, different from existing methods, all the virtual control laws need not be actually implemented at intermediate steps, and only one actual robust adaptive control law is constructed by approximating the lumped unknown function of the system with a single neural network at the last step. By this approach, the structure of the designed controller is much simpler since the causes for the problem of complexity growing in existing methods are eliminated. Stability analysis shows that the proposed scheme can guarantee the uniform ultimate boundedness of all the closed-loop system signals, and the steady-state tracking error can be made arbitrarily small by appropriately choosing control parameters. Simulation studies demonstrate the effectiveness and merits of the proposed approach. In this paper, a robust adaptive neural control design approach is presented for a class of perturbed strict-feedback nonlinear systems with unknown dead-zone. In the controller design, different from existing methods, all the virtual control laws need not be actually implemented at intermediate steps, and only one actual robust adaptive control law is constructed by approximating the lumped unknown function of the system with a single neural network at the last step. By this approach, the structure of the designed controller is much simpler since the causes for the problem of complexity growing in existing methods are eliminated. Stability analysis shows that the proposed scheme can guarantee the uniform ultimate boundedness of all the closed-loop system signals, and the steady-state tracking error can be made arbitrarily small by appropriately choosing control parameters. Simulation studies demonstrate the effectiveness and merits of the proposed approach. Single neural network Elsevier Uncertain strict-feedback nonlinear systems Elsevier Disturbances Elsevier Robust adaptive control Elsevier Dead-zone Elsevier Wang, Dan oth Wang, Mingxin oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:145 year:2014 day:5 month:12 pages:221-229 extent:9 https://doi.org/10.1016/j.neucom.2014.05.039 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 145 2014 5 1205 221-229 9 045F 610 |
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10.1016/j.neucom.2014.05.039 doi GBVA2014014000023.pica (DE-627)ELV017635500 (ELSEVIER)S0925-2312(14)00686-9 DE-627 ger DE-627 rakwb eng 610 610 DE-600 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Sun, Gang verfasserin aut Robust adaptive neural network control of a class of uncertain strict-feedback nonlinear systems with unknown dead-zone and disturbances 2014transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, a robust adaptive neural control design approach is presented for a class of perturbed strict-feedback nonlinear systems with unknown dead-zone. In the controller design, different from existing methods, all the virtual control laws need not be actually implemented at intermediate steps, and only one actual robust adaptive control law is constructed by approximating the lumped unknown function of the system with a single neural network at the last step. By this approach, the structure of the designed controller is much simpler since the causes for the problem of complexity growing in existing methods are eliminated. Stability analysis shows that the proposed scheme can guarantee the uniform ultimate boundedness of all the closed-loop system signals, and the steady-state tracking error can be made arbitrarily small by appropriately choosing control parameters. Simulation studies demonstrate the effectiveness and merits of the proposed approach. In this paper, a robust adaptive neural control design approach is presented for a class of perturbed strict-feedback nonlinear systems with unknown dead-zone. In the controller design, different from existing methods, all the virtual control laws need not be actually implemented at intermediate steps, and only one actual robust adaptive control law is constructed by approximating the lumped unknown function of the system with a single neural network at the last step. By this approach, the structure of the designed controller is much simpler since the causes for the problem of complexity growing in existing methods are eliminated. Stability analysis shows that the proposed scheme can guarantee the uniform ultimate boundedness of all the closed-loop system signals, and the steady-state tracking error can be made arbitrarily small by appropriately choosing control parameters. Simulation studies demonstrate the effectiveness and merits of the proposed approach. Single neural network Elsevier Uncertain strict-feedback nonlinear systems Elsevier Disturbances Elsevier Robust adaptive control Elsevier Dead-zone Elsevier Wang, Dan oth Wang, Mingxin oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:145 year:2014 day:5 month:12 pages:221-229 extent:9 https://doi.org/10.1016/j.neucom.2014.05.039 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 145 2014 5 1205 221-229 9 045F 610 |
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10.1016/j.neucom.2014.05.039 doi GBVA2014014000023.pica (DE-627)ELV017635500 (ELSEVIER)S0925-2312(14)00686-9 DE-627 ger DE-627 rakwb eng 610 610 DE-600 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Sun, Gang verfasserin aut Robust adaptive neural network control of a class of uncertain strict-feedback nonlinear systems with unknown dead-zone and disturbances 2014transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, a robust adaptive neural control design approach is presented for a class of perturbed strict-feedback nonlinear systems with unknown dead-zone. In the controller design, different from existing methods, all the virtual control laws need not be actually implemented at intermediate steps, and only one actual robust adaptive control law is constructed by approximating the lumped unknown function of the system with a single neural network at the last step. By this approach, the structure of the designed controller is much simpler since the causes for the problem of complexity growing in existing methods are eliminated. Stability analysis shows that the proposed scheme can guarantee the uniform ultimate boundedness of all the closed-loop system signals, and the steady-state tracking error can be made arbitrarily small by appropriately choosing control parameters. Simulation studies demonstrate the effectiveness and merits of the proposed approach. In this paper, a robust adaptive neural control design approach is presented for a class of perturbed strict-feedback nonlinear systems with unknown dead-zone. In the controller design, different from existing methods, all the virtual control laws need not be actually implemented at intermediate steps, and only one actual robust adaptive control law is constructed by approximating the lumped unknown function of the system with a single neural network at the last step. By this approach, the structure of the designed controller is much simpler since the causes for the problem of complexity growing in existing methods are eliminated. Stability analysis shows that the proposed scheme can guarantee the uniform ultimate boundedness of all the closed-loop system signals, and the steady-state tracking error can be made arbitrarily small by appropriately choosing control parameters. Simulation studies demonstrate the effectiveness and merits of the proposed approach. Single neural network Elsevier Uncertain strict-feedback nonlinear systems Elsevier Disturbances Elsevier Robust adaptive control Elsevier Dead-zone Elsevier Wang, Dan oth Wang, Mingxin oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:145 year:2014 day:5 month:12 pages:221-229 extent:9 https://doi.org/10.1016/j.neucom.2014.05.039 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 145 2014 5 1205 221-229 9 045F 610 |
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10.1016/j.neucom.2014.05.039 doi GBVA2014014000023.pica (DE-627)ELV017635500 (ELSEVIER)S0925-2312(14)00686-9 DE-627 ger DE-627 rakwb eng 610 610 DE-600 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Sun, Gang verfasserin aut Robust adaptive neural network control of a class of uncertain strict-feedback nonlinear systems with unknown dead-zone and disturbances 2014transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, a robust adaptive neural control design approach is presented for a class of perturbed strict-feedback nonlinear systems with unknown dead-zone. In the controller design, different from existing methods, all the virtual control laws need not be actually implemented at intermediate steps, and only one actual robust adaptive control law is constructed by approximating the lumped unknown function of the system with a single neural network at the last step. By this approach, the structure of the designed controller is much simpler since the causes for the problem of complexity growing in existing methods are eliminated. Stability analysis shows that the proposed scheme can guarantee the uniform ultimate boundedness of all the closed-loop system signals, and the steady-state tracking error can be made arbitrarily small by appropriately choosing control parameters. Simulation studies demonstrate the effectiveness and merits of the proposed approach. In this paper, a robust adaptive neural control design approach is presented for a class of perturbed strict-feedback nonlinear systems with unknown dead-zone. In the controller design, different from existing methods, all the virtual control laws need not be actually implemented at intermediate steps, and only one actual robust adaptive control law is constructed by approximating the lumped unknown function of the system with a single neural network at the last step. By this approach, the structure of the designed controller is much simpler since the causes for the problem of complexity growing in existing methods are eliminated. Stability analysis shows that the proposed scheme can guarantee the uniform ultimate boundedness of all the closed-loop system signals, and the steady-state tracking error can be made arbitrarily small by appropriately choosing control parameters. Simulation studies demonstrate the effectiveness and merits of the proposed approach. Single neural network Elsevier Uncertain strict-feedback nonlinear systems Elsevier Disturbances Elsevier Robust adaptive control Elsevier Dead-zone Elsevier Wang, Dan oth Wang, Mingxin oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:145 year:2014 day:5 month:12 pages:221-229 extent:9 https://doi.org/10.1016/j.neucom.2014.05.039 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 145 2014 5 1205 221-229 9 045F 610 |
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10.1016/j.neucom.2014.05.039 doi GBVA2014014000023.pica (DE-627)ELV017635500 (ELSEVIER)S0925-2312(14)00686-9 DE-627 ger DE-627 rakwb eng 610 610 DE-600 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Sun, Gang verfasserin aut Robust adaptive neural network control of a class of uncertain strict-feedback nonlinear systems with unknown dead-zone and disturbances 2014transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, a robust adaptive neural control design approach is presented for a class of perturbed strict-feedback nonlinear systems with unknown dead-zone. In the controller design, different from existing methods, all the virtual control laws need not be actually implemented at intermediate steps, and only one actual robust adaptive control law is constructed by approximating the lumped unknown function of the system with a single neural network at the last step. By this approach, the structure of the designed controller is much simpler since the causes for the problem of complexity growing in existing methods are eliminated. Stability analysis shows that the proposed scheme can guarantee the uniform ultimate boundedness of all the closed-loop system signals, and the steady-state tracking error can be made arbitrarily small by appropriately choosing control parameters. Simulation studies demonstrate the effectiveness and merits of the proposed approach. In this paper, a robust adaptive neural control design approach is presented for a class of perturbed strict-feedback nonlinear systems with unknown dead-zone. In the controller design, different from existing methods, all the virtual control laws need not be actually implemented at intermediate steps, and only one actual robust adaptive control law is constructed by approximating the lumped unknown function of the system with a single neural network at the last step. By this approach, the structure of the designed controller is much simpler since the causes for the problem of complexity growing in existing methods are eliminated. Stability analysis shows that the proposed scheme can guarantee the uniform ultimate boundedness of all the closed-loop system signals, and the steady-state tracking error can be made arbitrarily small by appropriately choosing control parameters. Simulation studies demonstrate the effectiveness and merits of the proposed approach. Single neural network Elsevier Uncertain strict-feedback nonlinear systems Elsevier Disturbances Elsevier Robust adaptive control Elsevier Dead-zone Elsevier Wang, Dan oth Wang, Mingxin oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:145 year:2014 day:5 month:12 pages:221-229 extent:9 https://doi.org/10.1016/j.neucom.2014.05.039 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 145 2014 5 1205 221-229 9 045F 610 |
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The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast |
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The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast |
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The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast |
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robust adaptive neural network control of a class of uncertain strict-feedback nonlinear systems with unknown dead-zone and disturbances |
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Robust adaptive neural network control of a class of uncertain strict-feedback nonlinear systems with unknown dead-zone and disturbances |
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In this paper, a robust adaptive neural control design approach is presented for a class of perturbed strict-feedback nonlinear systems with unknown dead-zone. In the controller design, different from existing methods, all the virtual control laws need not be actually implemented at intermediate steps, and only one actual robust adaptive control law is constructed by approximating the lumped unknown function of the system with a single neural network at the last step. By this approach, the structure of the designed controller is much simpler since the causes for the problem of complexity growing in existing methods are eliminated. Stability analysis shows that the proposed scheme can guarantee the uniform ultimate boundedness of all the closed-loop system signals, and the steady-state tracking error can be made arbitrarily small by appropriately choosing control parameters. Simulation studies demonstrate the effectiveness and merits of the proposed approach. |
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In this paper, a robust adaptive neural control design approach is presented for a class of perturbed strict-feedback nonlinear systems with unknown dead-zone. In the controller design, different from existing methods, all the virtual control laws need not be actually implemented at intermediate steps, and only one actual robust adaptive control law is constructed by approximating the lumped unknown function of the system with a single neural network at the last step. By this approach, the structure of the designed controller is much simpler since the causes for the problem of complexity growing in existing methods are eliminated. Stability analysis shows that the proposed scheme can guarantee the uniform ultimate boundedness of all the closed-loop system signals, and the steady-state tracking error can be made arbitrarily small by appropriately choosing control parameters. Simulation studies demonstrate the effectiveness and merits of the proposed approach. |
| abstract_unstemmed |
In this paper, a robust adaptive neural control design approach is presented for a class of perturbed strict-feedback nonlinear systems with unknown dead-zone. In the controller design, different from existing methods, all the virtual control laws need not be actually implemented at intermediate steps, and only one actual robust adaptive control law is constructed by approximating the lumped unknown function of the system with a single neural network at the last step. By this approach, the structure of the designed controller is much simpler since the causes for the problem of complexity growing in existing methods are eliminated. Stability analysis shows that the proposed scheme can guarantee the uniform ultimate boundedness of all the closed-loop system signals, and the steady-state tracking error can be made arbitrarily small by appropriately choosing control parameters. Simulation studies demonstrate the effectiveness and merits of the proposed approach. |
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Robust adaptive neural network control of a class of uncertain strict-feedback nonlinear systems with unknown dead-zone and disturbances |
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