Neural network controller for robotic motion control
Abstract Since a robotic manipulator has a complicated mathematical model, it is difficult to design a control system based on the complicated multi-variable nonlinear coupling dynamic model. Intelligent controllers using fuzzy and neural network approaches do not need a real mathematical model to d...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Huang, Shiuh-Jer [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
1996 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag London Limited 1996 |
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| Übergeordnetes Werk: |
Enthalten in: The international journal of advanced manufacturing technology - Springer-Verlag, 1985, 12(1996), 6 vom: Nov., Seite 450-454 |
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| Übergeordnetes Werk: |
volume:12 ; year:1996 ; number:6 ; month:11 ; pages:450-454 |
| Links: |
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| DOI / URN: |
10.1007/BF01186934 |
|---|
| Katalog-ID: |
OLC2025987021 |
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| 520 | |a Abstract Since a robotic manipulator has a complicated mathematical model, it is difficult to design a control system based on the complicated multi-variable nonlinear coupling dynamic model. Intelligent controllers using fuzzy and neural network approaches do not need a real mathematical model to design the control structure and have attracted the attention of robotic control researchers recently. A traditional fuzzy logic controller does not have learning capability and it needs a lot of effort to search for the optimal control rules and the shapes of membership functions. Owing to the time-varying behaviour of the system, the required fine tracking accuracy is difficult to achieve by adjusting the fuzzy rules only. The implementation problems of neural network control are the initial training and initial transient stability. In order to improve the position control accuracy and system robustness for industrial applications, a neural controller is first trained off-line by using the input and output (I/O) data of a traditional fuzzy controller. Then the neural controller is implemented on a five-degrees-of-freedom robot with a back propagation algorithm for online adjustment. The experimental results show that this neural network controller achieved the required trajectory tracking accuracy after 15 on-line operations. | ||
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10.1007/BF01186934 doi (DE-627)OLC2025987021 (DE-He213)BF01186934-p DE-627 ger DE-627 rakwb eng 670 VZ Huang, Shiuh-Jer verfasserin aut Neural network controller for robotic motion control 1996 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Limited 1996 Abstract Since a robotic manipulator has a complicated mathematical model, it is difficult to design a control system based on the complicated multi-variable nonlinear coupling dynamic model. Intelligent controllers using fuzzy and neural network approaches do not need a real mathematical model to design the control structure and have attracted the attention of robotic control researchers recently. A traditional fuzzy logic controller does not have learning capability and it needs a lot of effort to search for the optimal control rules and the shapes of membership functions. Owing to the time-varying behaviour of the system, the required fine tracking accuracy is difficult to achieve by adjusting the fuzzy rules only. The implementation problems of neural network control are the initial training and initial transient stability. In order to improve the position control accuracy and system robustness for industrial applications, a neural controller is first trained off-line by using the input and output (I/O) data of a traditional fuzzy controller. Then the neural controller is implemented on a five-degrees-of-freedom robot with a back propagation algorithm for online adjustment. The experimental results show that this neural network controller achieved the required trajectory tracking accuracy after 15 on-line operations. Intelligent control Neural controller Robot Hu, Chih-Feng aut Enthalten in The international journal of advanced manufacturing technology Springer-Verlag, 1985 12(1996), 6 vom: Nov., Seite 450-454 (DE-627)129185299 (DE-600)52651-4 (DE-576)014456192 0268-3768 nnns volume:12 year:1996 number:6 month:11 pages:450-454 https://doi.org/10.1007/BF01186934 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_20 GBV_ILN_21 GBV_ILN_23 GBV_ILN_32 GBV_ILN_70 GBV_ILN_132 GBV_ILN_136 GBV_ILN_150 GBV_ILN_161 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2048 GBV_ILN_2241 GBV_ILN_2333 GBV_ILN_4046 GBV_ILN_4277 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4319 AR 12 1996 6 11 450-454 |
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10.1007/BF01186934 doi (DE-627)OLC2025987021 (DE-He213)BF01186934-p DE-627 ger DE-627 rakwb eng 670 VZ Huang, Shiuh-Jer verfasserin aut Neural network controller for robotic motion control 1996 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Limited 1996 Abstract Since a robotic manipulator has a complicated mathematical model, it is difficult to design a control system based on the complicated multi-variable nonlinear coupling dynamic model. Intelligent controllers using fuzzy and neural network approaches do not need a real mathematical model to design the control structure and have attracted the attention of robotic control researchers recently. A traditional fuzzy logic controller does not have learning capability and it needs a lot of effort to search for the optimal control rules and the shapes of membership functions. Owing to the time-varying behaviour of the system, the required fine tracking accuracy is difficult to achieve by adjusting the fuzzy rules only. The implementation problems of neural network control are the initial training and initial transient stability. In order to improve the position control accuracy and system robustness for industrial applications, a neural controller is first trained off-line by using the input and output (I/O) data of a traditional fuzzy controller. Then the neural controller is implemented on a five-degrees-of-freedom robot with a back propagation algorithm for online adjustment. The experimental results show that this neural network controller achieved the required trajectory tracking accuracy after 15 on-line operations. Intelligent control Neural controller Robot Hu, Chih-Feng aut Enthalten in The international journal of advanced manufacturing technology Springer-Verlag, 1985 12(1996), 6 vom: Nov., Seite 450-454 (DE-627)129185299 (DE-600)52651-4 (DE-576)014456192 0268-3768 nnns volume:12 year:1996 number:6 month:11 pages:450-454 https://doi.org/10.1007/BF01186934 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_20 GBV_ILN_21 GBV_ILN_23 GBV_ILN_32 GBV_ILN_70 GBV_ILN_132 GBV_ILN_136 GBV_ILN_150 GBV_ILN_161 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2048 GBV_ILN_2241 GBV_ILN_2333 GBV_ILN_4046 GBV_ILN_4277 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4319 AR 12 1996 6 11 450-454 |
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10.1007/BF01186934 doi (DE-627)OLC2025987021 (DE-He213)BF01186934-p DE-627 ger DE-627 rakwb eng 670 VZ Huang, Shiuh-Jer verfasserin aut Neural network controller for robotic motion control 1996 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Limited 1996 Abstract Since a robotic manipulator has a complicated mathematical model, it is difficult to design a control system based on the complicated multi-variable nonlinear coupling dynamic model. Intelligent controllers using fuzzy and neural network approaches do not need a real mathematical model to design the control structure and have attracted the attention of robotic control researchers recently. A traditional fuzzy logic controller does not have learning capability and it needs a lot of effort to search for the optimal control rules and the shapes of membership functions. Owing to the time-varying behaviour of the system, the required fine tracking accuracy is difficult to achieve by adjusting the fuzzy rules only. The implementation problems of neural network control are the initial training and initial transient stability. In order to improve the position control accuracy and system robustness for industrial applications, a neural controller is first trained off-line by using the input and output (I/O) data of a traditional fuzzy controller. Then the neural controller is implemented on a five-degrees-of-freedom robot with a back propagation algorithm for online adjustment. The experimental results show that this neural network controller achieved the required trajectory tracking accuracy after 15 on-line operations. Intelligent control Neural controller Robot Hu, Chih-Feng aut Enthalten in The international journal of advanced manufacturing technology Springer-Verlag, 1985 12(1996), 6 vom: Nov., Seite 450-454 (DE-627)129185299 (DE-600)52651-4 (DE-576)014456192 0268-3768 nnns volume:12 year:1996 number:6 month:11 pages:450-454 https://doi.org/10.1007/BF01186934 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_20 GBV_ILN_21 GBV_ILN_23 GBV_ILN_32 GBV_ILN_70 GBV_ILN_132 GBV_ILN_136 GBV_ILN_150 GBV_ILN_161 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2048 GBV_ILN_2241 GBV_ILN_2333 GBV_ILN_4046 GBV_ILN_4277 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4319 AR 12 1996 6 11 450-454 |
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10.1007/BF01186934 doi (DE-627)OLC2025987021 (DE-He213)BF01186934-p DE-627 ger DE-627 rakwb eng 670 VZ Huang, Shiuh-Jer verfasserin aut Neural network controller for robotic motion control 1996 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Limited 1996 Abstract Since a robotic manipulator has a complicated mathematical model, it is difficult to design a control system based on the complicated multi-variable nonlinear coupling dynamic model. Intelligent controllers using fuzzy and neural network approaches do not need a real mathematical model to design the control structure and have attracted the attention of robotic control researchers recently. A traditional fuzzy logic controller does not have learning capability and it needs a lot of effort to search for the optimal control rules and the shapes of membership functions. Owing to the time-varying behaviour of the system, the required fine tracking accuracy is difficult to achieve by adjusting the fuzzy rules only. The implementation problems of neural network control are the initial training and initial transient stability. In order to improve the position control accuracy and system robustness for industrial applications, a neural controller is first trained off-line by using the input and output (I/O) data of a traditional fuzzy controller. Then the neural controller is implemented on a five-degrees-of-freedom robot with a back propagation algorithm for online adjustment. The experimental results show that this neural network controller achieved the required trajectory tracking accuracy after 15 on-line operations. Intelligent control Neural controller Robot Hu, Chih-Feng aut Enthalten in The international journal of advanced manufacturing technology Springer-Verlag, 1985 12(1996), 6 vom: Nov., Seite 450-454 (DE-627)129185299 (DE-600)52651-4 (DE-576)014456192 0268-3768 nnns volume:12 year:1996 number:6 month:11 pages:450-454 https://doi.org/10.1007/BF01186934 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_20 GBV_ILN_21 GBV_ILN_23 GBV_ILN_32 GBV_ILN_70 GBV_ILN_132 GBV_ILN_136 GBV_ILN_150 GBV_ILN_161 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2048 GBV_ILN_2241 GBV_ILN_2333 GBV_ILN_4046 GBV_ILN_4277 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4319 AR 12 1996 6 11 450-454 |
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10.1007/BF01186934 doi (DE-627)OLC2025987021 (DE-He213)BF01186934-p DE-627 ger DE-627 rakwb eng 670 VZ Huang, Shiuh-Jer verfasserin aut Neural network controller for robotic motion control 1996 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Limited 1996 Abstract Since a robotic manipulator has a complicated mathematical model, it is difficult to design a control system based on the complicated multi-variable nonlinear coupling dynamic model. Intelligent controllers using fuzzy and neural network approaches do not need a real mathematical model to design the control structure and have attracted the attention of robotic control researchers recently. A traditional fuzzy logic controller does not have learning capability and it needs a lot of effort to search for the optimal control rules and the shapes of membership functions. Owing to the time-varying behaviour of the system, the required fine tracking accuracy is difficult to achieve by adjusting the fuzzy rules only. The implementation problems of neural network control are the initial training and initial transient stability. In order to improve the position control accuracy and system robustness for industrial applications, a neural controller is first trained off-line by using the input and output (I/O) data of a traditional fuzzy controller. Then the neural controller is implemented on a five-degrees-of-freedom robot with a back propagation algorithm for online adjustment. The experimental results show that this neural network controller achieved the required trajectory tracking accuracy after 15 on-line operations. Intelligent control Neural controller Robot Hu, Chih-Feng aut Enthalten in The international journal of advanced manufacturing technology Springer-Verlag, 1985 12(1996), 6 vom: Nov., Seite 450-454 (DE-627)129185299 (DE-600)52651-4 (DE-576)014456192 0268-3768 nnns volume:12 year:1996 number:6 month:11 pages:450-454 https://doi.org/10.1007/BF01186934 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_20 GBV_ILN_21 GBV_ILN_23 GBV_ILN_32 GBV_ILN_70 GBV_ILN_132 GBV_ILN_136 GBV_ILN_150 GBV_ILN_161 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2048 GBV_ILN_2241 GBV_ILN_2333 GBV_ILN_4046 GBV_ILN_4277 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4319 AR 12 1996 6 11 450-454 |
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Neural network controller for robotic motion control |
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Neural network controller for robotic motion control |
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Huang, Shiuh-Jer |
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1996 |
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Huang, Shiuh-Jer Hu, Chih-Feng |
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10.1007/BF01186934 |
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670 |
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neural network controller for robotic motion control |
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Neural network controller for robotic motion control |
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Abstract Since a robotic manipulator has a complicated mathematical model, it is difficult to design a control system based on the complicated multi-variable nonlinear coupling dynamic model. Intelligent controllers using fuzzy and neural network approaches do not need a real mathematical model to design the control structure and have attracted the attention of robotic control researchers recently. A traditional fuzzy logic controller does not have learning capability and it needs a lot of effort to search for the optimal control rules and the shapes of membership functions. Owing to the time-varying behaviour of the system, the required fine tracking accuracy is difficult to achieve by adjusting the fuzzy rules only. The implementation problems of neural network control are the initial training and initial transient stability. In order to improve the position control accuracy and system robustness for industrial applications, a neural controller is first trained off-line by using the input and output (I/O) data of a traditional fuzzy controller. Then the neural controller is implemented on a five-degrees-of-freedom robot with a back propagation algorithm for online adjustment. The experimental results show that this neural network controller achieved the required trajectory tracking accuracy after 15 on-line operations. © Springer-Verlag London Limited 1996 |
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Abstract Since a robotic manipulator has a complicated mathematical model, it is difficult to design a control system based on the complicated multi-variable nonlinear coupling dynamic model. Intelligent controllers using fuzzy and neural network approaches do not need a real mathematical model to design the control structure and have attracted the attention of robotic control researchers recently. A traditional fuzzy logic controller does not have learning capability and it needs a lot of effort to search for the optimal control rules and the shapes of membership functions. Owing to the time-varying behaviour of the system, the required fine tracking accuracy is difficult to achieve by adjusting the fuzzy rules only. The implementation problems of neural network control are the initial training and initial transient stability. In order to improve the position control accuracy and system robustness for industrial applications, a neural controller is first trained off-line by using the input and output (I/O) data of a traditional fuzzy controller. Then the neural controller is implemented on a five-degrees-of-freedom robot with a back propagation algorithm for online adjustment. The experimental results show that this neural network controller achieved the required trajectory tracking accuracy after 15 on-line operations. © Springer-Verlag London Limited 1996 |
| abstract_unstemmed |
Abstract Since a robotic manipulator has a complicated mathematical model, it is difficult to design a control system based on the complicated multi-variable nonlinear coupling dynamic model. Intelligent controllers using fuzzy and neural network approaches do not need a real mathematical model to design the control structure and have attracted the attention of robotic control researchers recently. A traditional fuzzy logic controller does not have learning capability and it needs a lot of effort to search for the optimal control rules and the shapes of membership functions. Owing to the time-varying behaviour of the system, the required fine tracking accuracy is difficult to achieve by adjusting the fuzzy rules only. The implementation problems of neural network control are the initial training and initial transient stability. In order to improve the position control accuracy and system robustness for industrial applications, a neural controller is first trained off-line by using the input and output (I/O) data of a traditional fuzzy controller. Then the neural controller is implemented on a five-degrees-of-freedom robot with a back propagation algorithm for online adjustment. The experimental results show that this neural network controller achieved the required trajectory tracking accuracy after 15 on-line operations. © Springer-Verlag London Limited 1996 |
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Neural network controller for robotic motion control |
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