Adaptive wavelet neural network controller for active suppression control of a diaphragm-type pneumatic vibration isolator
Abstract This paper applies an intelligent control scheme to investigate a diaphragm-type pneumatic vibration isolation (PVI) problem. Adaptive wavelet neural network (AWNN) control is employed to control the PVI system which has inherited nonlinear and time-varying system characteristics. Since AWN...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Chen, Hung-Yi [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
Adaptive wavelet neural network |
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| Anmerkung: |
© Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag Berlin Heidelberg 2017 |
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| Übergeordnetes Werk: |
Enthalten in: International Journal of Control, Automation and Systems - Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers, 2009, 15(2017), 3 vom: 27. März, Seite 1456-1465 |
|---|---|
| Übergeordnetes Werk: |
volume:15 ; year:2017 ; number:3 ; day:27 ; month:03 ; pages:1456-1465 |
| Links: |
|---|
| DOI / URN: |
10.1007/s12555-014-0428-2 |
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SPR026433885 |
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| 520 | |a Abstract This paper applies an intelligent control scheme to investigate a diaphragm-type pneumatic vibration isolation (PVI) problem. Adaptive wavelet neural network (AWNN) control is employed to control the PVI system which has inherited nonlinear and time-varying system characteristics. Since AWNN has excellent approximation capability originating from wavelet decomposition property and online learning ability stemming from neural networks, the method is especially suitable for the PVI control applications. In this paper, the adaptive learning rates are derived based on the Lyapunov stability theorem. Therefore, the stability of the closed-loop system can be assured. To validate the proposed method, a composite control scheme using pressure and velocity measurements as feedback signals is implemented. During experimental investigations, sinusoidal excitation with the excitation frequency close to the resonance and random-like signal are input on a floor base to simulate ground vibrations. Performances obtained from the proposed control scheme are compared with those obtained from passive isolation and PID scheme to illustrate the effectiveness of the proposed intelligent control. | ||
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10.1007/s12555-014-0428-2 doi (DE-627)SPR026433885 (SPR)s12555-014-0428-2-e DE-627 ger DE-627 rakwb eng Chen, Hung-Yi verfasserin aut Adaptive wavelet neural network controller for active suppression control of a diaphragm-type pneumatic vibration isolator 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag Berlin Heidelberg 2017 Abstract This paper applies an intelligent control scheme to investigate a diaphragm-type pneumatic vibration isolation (PVI) problem. Adaptive wavelet neural network (AWNN) control is employed to control the PVI system which has inherited nonlinear and time-varying system characteristics. Since AWNN has excellent approximation capability originating from wavelet decomposition property and online learning ability stemming from neural networks, the method is especially suitable for the PVI control applications. In this paper, the adaptive learning rates are derived based on the Lyapunov stability theorem. Therefore, the stability of the closed-loop system can be assured. To validate the proposed method, a composite control scheme using pressure and velocity measurements as feedback signals is implemented. During experimental investigations, sinusoidal excitation with the excitation frequency close to the resonance and random-like signal are input on a floor base to simulate ground vibrations. Performances obtained from the proposed control scheme are compared with those obtained from passive isolation and PID scheme to illustrate the effectiveness of the proposed intelligent control. Adaptive wavelet neural network (dpeaa)DE-He213 diaphragm-type pneumatic vibration isolation system (dpeaa)DE-He213 pressure and velocity measurements (dpeaa)DE-He213 Liang, Jin-Wei aut Enthalten in International Journal of Control, Automation and Systems Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers, 2009 15(2017), 3 vom: 27. März, Seite 1456-1465 (DE-627)SPR026303256 nnns volume:15 year:2017 number:3 day:27 month:03 pages:1456-1465 https://dx.doi.org/10.1007/s12555-014-0428-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_21 GBV_ILN_24 GBV_ILN_72 GBV_ILN_181 GBV_ILN_496 GBV_ILN_2002 GBV_ILN_2003 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2060 GBV_ILN_2470 AR 15 2017 3 27 03 1456-1465 |
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10.1007/s12555-014-0428-2 doi (DE-627)SPR026433885 (SPR)s12555-014-0428-2-e DE-627 ger DE-627 rakwb eng Chen, Hung-Yi verfasserin aut Adaptive wavelet neural network controller for active suppression control of a diaphragm-type pneumatic vibration isolator 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag Berlin Heidelberg 2017 Abstract This paper applies an intelligent control scheme to investigate a diaphragm-type pneumatic vibration isolation (PVI) problem. Adaptive wavelet neural network (AWNN) control is employed to control the PVI system which has inherited nonlinear and time-varying system characteristics. Since AWNN has excellent approximation capability originating from wavelet decomposition property and online learning ability stemming from neural networks, the method is especially suitable for the PVI control applications. In this paper, the adaptive learning rates are derived based on the Lyapunov stability theorem. Therefore, the stability of the closed-loop system can be assured. To validate the proposed method, a composite control scheme using pressure and velocity measurements as feedback signals is implemented. During experimental investigations, sinusoidal excitation with the excitation frequency close to the resonance and random-like signal are input on a floor base to simulate ground vibrations. Performances obtained from the proposed control scheme are compared with those obtained from passive isolation and PID scheme to illustrate the effectiveness of the proposed intelligent control. Adaptive wavelet neural network (dpeaa)DE-He213 diaphragm-type pneumatic vibration isolation system (dpeaa)DE-He213 pressure and velocity measurements (dpeaa)DE-He213 Liang, Jin-Wei aut Enthalten in International Journal of Control, Automation and Systems Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers, 2009 15(2017), 3 vom: 27. März, Seite 1456-1465 (DE-627)SPR026303256 nnns volume:15 year:2017 number:3 day:27 month:03 pages:1456-1465 https://dx.doi.org/10.1007/s12555-014-0428-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_21 GBV_ILN_24 GBV_ILN_72 GBV_ILN_181 GBV_ILN_496 GBV_ILN_2002 GBV_ILN_2003 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2060 GBV_ILN_2470 AR 15 2017 3 27 03 1456-1465 |
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10.1007/s12555-014-0428-2 doi (DE-627)SPR026433885 (SPR)s12555-014-0428-2-e DE-627 ger DE-627 rakwb eng Chen, Hung-Yi verfasserin aut Adaptive wavelet neural network controller for active suppression control of a diaphragm-type pneumatic vibration isolator 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag Berlin Heidelberg 2017 Abstract This paper applies an intelligent control scheme to investigate a diaphragm-type pneumatic vibration isolation (PVI) problem. Adaptive wavelet neural network (AWNN) control is employed to control the PVI system which has inherited nonlinear and time-varying system characteristics. Since AWNN has excellent approximation capability originating from wavelet decomposition property and online learning ability stemming from neural networks, the method is especially suitable for the PVI control applications. In this paper, the adaptive learning rates are derived based on the Lyapunov stability theorem. Therefore, the stability of the closed-loop system can be assured. To validate the proposed method, a composite control scheme using pressure and velocity measurements as feedback signals is implemented. During experimental investigations, sinusoidal excitation with the excitation frequency close to the resonance and random-like signal are input on a floor base to simulate ground vibrations. Performances obtained from the proposed control scheme are compared with those obtained from passive isolation and PID scheme to illustrate the effectiveness of the proposed intelligent control. Adaptive wavelet neural network (dpeaa)DE-He213 diaphragm-type pneumatic vibration isolation system (dpeaa)DE-He213 pressure and velocity measurements (dpeaa)DE-He213 Liang, Jin-Wei aut Enthalten in International Journal of Control, Automation and Systems Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers, 2009 15(2017), 3 vom: 27. März, Seite 1456-1465 (DE-627)SPR026303256 nnns volume:15 year:2017 number:3 day:27 month:03 pages:1456-1465 https://dx.doi.org/10.1007/s12555-014-0428-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_21 GBV_ILN_24 GBV_ILN_72 GBV_ILN_181 GBV_ILN_496 GBV_ILN_2002 GBV_ILN_2003 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2060 GBV_ILN_2470 AR 15 2017 3 27 03 1456-1465 |
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10.1007/s12555-014-0428-2 doi (DE-627)SPR026433885 (SPR)s12555-014-0428-2-e DE-627 ger DE-627 rakwb eng Chen, Hung-Yi verfasserin aut Adaptive wavelet neural network controller for active suppression control of a diaphragm-type pneumatic vibration isolator 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag Berlin Heidelberg 2017 Abstract This paper applies an intelligent control scheme to investigate a diaphragm-type pneumatic vibration isolation (PVI) problem. Adaptive wavelet neural network (AWNN) control is employed to control the PVI system which has inherited nonlinear and time-varying system characteristics. Since AWNN has excellent approximation capability originating from wavelet decomposition property and online learning ability stemming from neural networks, the method is especially suitable for the PVI control applications. In this paper, the adaptive learning rates are derived based on the Lyapunov stability theorem. Therefore, the stability of the closed-loop system can be assured. To validate the proposed method, a composite control scheme using pressure and velocity measurements as feedback signals is implemented. During experimental investigations, sinusoidal excitation with the excitation frequency close to the resonance and random-like signal are input on a floor base to simulate ground vibrations. Performances obtained from the proposed control scheme are compared with those obtained from passive isolation and PID scheme to illustrate the effectiveness of the proposed intelligent control. Adaptive wavelet neural network (dpeaa)DE-He213 diaphragm-type pneumatic vibration isolation system (dpeaa)DE-He213 pressure and velocity measurements (dpeaa)DE-He213 Liang, Jin-Wei aut Enthalten in International Journal of Control, Automation and Systems Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers, 2009 15(2017), 3 vom: 27. März, Seite 1456-1465 (DE-627)SPR026303256 nnns volume:15 year:2017 number:3 day:27 month:03 pages:1456-1465 https://dx.doi.org/10.1007/s12555-014-0428-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_21 GBV_ILN_24 GBV_ILN_72 GBV_ILN_181 GBV_ILN_496 GBV_ILN_2002 GBV_ILN_2003 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2060 GBV_ILN_2470 AR 15 2017 3 27 03 1456-1465 |
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10.1007/s12555-014-0428-2 doi (DE-627)SPR026433885 (SPR)s12555-014-0428-2-e DE-627 ger DE-627 rakwb eng Chen, Hung-Yi verfasserin aut Adaptive wavelet neural network controller for active suppression control of a diaphragm-type pneumatic vibration isolator 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag Berlin Heidelberg 2017 Abstract This paper applies an intelligent control scheme to investigate a diaphragm-type pneumatic vibration isolation (PVI) problem. Adaptive wavelet neural network (AWNN) control is employed to control the PVI system which has inherited nonlinear and time-varying system characteristics. Since AWNN has excellent approximation capability originating from wavelet decomposition property and online learning ability stemming from neural networks, the method is especially suitable for the PVI control applications. In this paper, the adaptive learning rates are derived based on the Lyapunov stability theorem. Therefore, the stability of the closed-loop system can be assured. To validate the proposed method, a composite control scheme using pressure and velocity measurements as feedback signals is implemented. During experimental investigations, sinusoidal excitation with the excitation frequency close to the resonance and random-like signal are input on a floor base to simulate ground vibrations. Performances obtained from the proposed control scheme are compared with those obtained from passive isolation and PID scheme to illustrate the effectiveness of the proposed intelligent control. Adaptive wavelet neural network (dpeaa)DE-He213 diaphragm-type pneumatic vibration isolation system (dpeaa)DE-He213 pressure and velocity measurements (dpeaa)DE-He213 Liang, Jin-Wei aut Enthalten in International Journal of Control, Automation and Systems Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers, 2009 15(2017), 3 vom: 27. März, Seite 1456-1465 (DE-627)SPR026303256 nnns volume:15 year:2017 number:3 day:27 month:03 pages:1456-1465 https://dx.doi.org/10.1007/s12555-014-0428-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_21 GBV_ILN_24 GBV_ILN_72 GBV_ILN_181 GBV_ILN_496 GBV_ILN_2002 GBV_ILN_2003 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2060 GBV_ILN_2470 AR 15 2017 3 27 03 1456-1465 |
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Chen, Hung-Yi Liang, Jin-Wei |
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Elektronische Aufsätze |
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Chen, Hung-Yi |
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10.1007/s12555-014-0428-2 |
| title_sort |
adaptive wavelet neural network controller for active suppression control of a diaphragm-type pneumatic vibration isolator |
| title_auth |
Adaptive wavelet neural network controller for active suppression control of a diaphragm-type pneumatic vibration isolator |
| abstract |
Abstract This paper applies an intelligent control scheme to investigate a diaphragm-type pneumatic vibration isolation (PVI) problem. Adaptive wavelet neural network (AWNN) control is employed to control the PVI system which has inherited nonlinear and time-varying system characteristics. Since AWNN has excellent approximation capability originating from wavelet decomposition property and online learning ability stemming from neural networks, the method is especially suitable for the PVI control applications. In this paper, the adaptive learning rates are derived based on the Lyapunov stability theorem. Therefore, the stability of the closed-loop system can be assured. To validate the proposed method, a composite control scheme using pressure and velocity measurements as feedback signals is implemented. During experimental investigations, sinusoidal excitation with the excitation frequency close to the resonance and random-like signal are input on a floor base to simulate ground vibrations. Performances obtained from the proposed control scheme are compared with those obtained from passive isolation and PID scheme to illustrate the effectiveness of the proposed intelligent control. © Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag Berlin Heidelberg 2017 |
| abstractGer |
Abstract This paper applies an intelligent control scheme to investigate a diaphragm-type pneumatic vibration isolation (PVI) problem. Adaptive wavelet neural network (AWNN) control is employed to control the PVI system which has inherited nonlinear and time-varying system characteristics. Since AWNN has excellent approximation capability originating from wavelet decomposition property and online learning ability stemming from neural networks, the method is especially suitable for the PVI control applications. In this paper, the adaptive learning rates are derived based on the Lyapunov stability theorem. Therefore, the stability of the closed-loop system can be assured. To validate the proposed method, a composite control scheme using pressure and velocity measurements as feedback signals is implemented. During experimental investigations, sinusoidal excitation with the excitation frequency close to the resonance and random-like signal are input on a floor base to simulate ground vibrations. Performances obtained from the proposed control scheme are compared with those obtained from passive isolation and PID scheme to illustrate the effectiveness of the proposed intelligent control. © Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag Berlin Heidelberg 2017 |
| abstract_unstemmed |
Abstract This paper applies an intelligent control scheme to investigate a diaphragm-type pneumatic vibration isolation (PVI) problem. Adaptive wavelet neural network (AWNN) control is employed to control the PVI system which has inherited nonlinear and time-varying system characteristics. Since AWNN has excellent approximation capability originating from wavelet decomposition property and online learning ability stemming from neural networks, the method is especially suitable for the PVI control applications. In this paper, the adaptive learning rates are derived based on the Lyapunov stability theorem. Therefore, the stability of the closed-loop system can be assured. To validate the proposed method, a composite control scheme using pressure and velocity measurements as feedback signals is implemented. During experimental investigations, sinusoidal excitation with the excitation frequency close to the resonance and random-like signal are input on a floor base to simulate ground vibrations. Performances obtained from the proposed control scheme are compared with those obtained from passive isolation and PID scheme to illustrate the effectiveness of the proposed intelligent control. © Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag Berlin Heidelberg 2017 |
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| title_short |
Adaptive wavelet neural network controller for active suppression control of a diaphragm-type pneumatic vibration isolator |
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https://dx.doi.org/10.1007/s12555-014-0428-2 |
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