Optimal Grey-Fuzzy Gain-Scheduler Design Using Taguchi-HGA Method
Abstract In this paper, the grey-fuzzy-gain-scheduling (GFGS) control scheme is proposed for making a nonlinear autonomous system to track a reference trajectory. The GFGS control scheme consists of two parts: the grey predictor and the fuzzy gain scheduling controller. An optimal combined method, i...
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| Autor*in: |
Hsieh, Chen-Huei [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2001 |
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| Schlagwörter: |
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| Anmerkung: |
© Kluwer Academic Publishers 2001 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of intelligent & robotic systems - Kluwer Academic Publishers, 1988, 32(2001), 3 vom: Nov., Seite 321-345 |
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| Übergeordnetes Werk: |
volume:32 ; year:2001 ; number:3 ; month:11 ; pages:321-345 |
| Links: |
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| DOI / URN: |
10.1023/A:1013911905035 |
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OLC2057167194 |
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| 520 | |a Abstract In this paper, the grey-fuzzy-gain-scheduling (GFGS) control scheme is proposed for making a nonlinear autonomous system to track a reference trajectory. The GFGS control scheme consists of two parts: the grey predictor and the fuzzy gain scheduling controller. An optimal combined method, i.e., Taguchi-hierarchical-genetic-algorithm (Taguchi-HGA), is presented in this paper to search for the optimal control parameters of both the grey predictor and the fuzzy gain scheduling controller (i.e., the sample size and grey constants of the grey predictor, the centers of the fuzzy regions, the left spread and the right spread of the membership functions, and the weighting matrices in the performance index of the linear quadratic regulator method) for guaranteeing stability and obtaining an optimal control performance. Computer simulations of a two-link robot arm example are performed to verify the effectiveness of the optimal GFGS control scheme designed by the Taguchi-HGA and to show that the optimal GFGS control scheme is superior to the existing optimal FGS (fuzzy-gain-scheduling) control scheme. | ||
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10.1023/A:1013911905035 doi (DE-627)OLC2057167194 (DE-He213)A:1013911905035-p DE-627 ger DE-627 rakwb eng 004 VZ Hsieh, Chen-Huei verfasserin aut Optimal Grey-Fuzzy Gain-Scheduler Design Using Taguchi-HGA Method 2001 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Kluwer Academic Publishers 2001 Abstract In this paper, the grey-fuzzy-gain-scheduling (GFGS) control scheme is proposed for making a nonlinear autonomous system to track a reference trajectory. The GFGS control scheme consists of two parts: the grey predictor and the fuzzy gain scheduling controller. An optimal combined method, i.e., Taguchi-hierarchical-genetic-algorithm (Taguchi-HGA), is presented in this paper to search for the optimal control parameters of both the grey predictor and the fuzzy gain scheduling controller (i.e., the sample size and grey constants of the grey predictor, the centers of the fuzzy regions, the left spread and the right spread of the membership functions, and the weighting matrices in the performance index of the linear quadratic regulator method) for guaranteeing stability and obtaining an optimal control performance. Computer simulations of a two-link robot arm example are performed to verify the effectiveness of the optimal GFGS control scheme designed by the Taguchi-HGA and to show that the optimal GFGS control scheme is superior to the existing optimal FGS (fuzzy-gain-scheduling) control scheme. Membership Function Autonomous System Combine Method Weighting Matrice Reference Trajectory Chou, Jyh-Horng aut Wu, Ying-Jeng aut Enthalten in Journal of intelligent & robotic systems Kluwer Academic Publishers, 1988 32(2001), 3 vom: Nov., Seite 321-345 (DE-627)130464864 (DE-600)740594-7 (DE-576)018667805 0921-0296 nnns volume:32 year:2001 number:3 month:11 pages:321-345 https://doi.org/10.1023/A:1013911905035 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_20 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2057 GBV_ILN_2244 GBV_ILN_4046 GBV_ILN_4266 GBV_ILN_4307 GBV_ILN_4318 AR 32 2001 3 11 321-345 |
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10.1023/A:1013911905035 doi (DE-627)OLC2057167194 (DE-He213)A:1013911905035-p DE-627 ger DE-627 rakwb eng 004 VZ Hsieh, Chen-Huei verfasserin aut Optimal Grey-Fuzzy Gain-Scheduler Design Using Taguchi-HGA Method 2001 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Kluwer Academic Publishers 2001 Abstract In this paper, the grey-fuzzy-gain-scheduling (GFGS) control scheme is proposed for making a nonlinear autonomous system to track a reference trajectory. The GFGS control scheme consists of two parts: the grey predictor and the fuzzy gain scheduling controller. An optimal combined method, i.e., Taguchi-hierarchical-genetic-algorithm (Taguchi-HGA), is presented in this paper to search for the optimal control parameters of both the grey predictor and the fuzzy gain scheduling controller (i.e., the sample size and grey constants of the grey predictor, the centers of the fuzzy regions, the left spread and the right spread of the membership functions, and the weighting matrices in the performance index of the linear quadratic regulator method) for guaranteeing stability and obtaining an optimal control performance. Computer simulations of a two-link robot arm example are performed to verify the effectiveness of the optimal GFGS control scheme designed by the Taguchi-HGA and to show that the optimal GFGS control scheme is superior to the existing optimal FGS (fuzzy-gain-scheduling) control scheme. Membership Function Autonomous System Combine Method Weighting Matrice Reference Trajectory Chou, Jyh-Horng aut Wu, Ying-Jeng aut Enthalten in Journal of intelligent & robotic systems Kluwer Academic Publishers, 1988 32(2001), 3 vom: Nov., Seite 321-345 (DE-627)130464864 (DE-600)740594-7 (DE-576)018667805 0921-0296 nnns volume:32 year:2001 number:3 month:11 pages:321-345 https://doi.org/10.1023/A:1013911905035 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_20 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2057 GBV_ILN_2244 GBV_ILN_4046 GBV_ILN_4266 GBV_ILN_4307 GBV_ILN_4318 AR 32 2001 3 11 321-345 |
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10.1023/A:1013911905035 doi (DE-627)OLC2057167194 (DE-He213)A:1013911905035-p DE-627 ger DE-627 rakwb eng 004 VZ Hsieh, Chen-Huei verfasserin aut Optimal Grey-Fuzzy Gain-Scheduler Design Using Taguchi-HGA Method 2001 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Kluwer Academic Publishers 2001 Abstract In this paper, the grey-fuzzy-gain-scheduling (GFGS) control scheme is proposed for making a nonlinear autonomous system to track a reference trajectory. The GFGS control scheme consists of two parts: the grey predictor and the fuzzy gain scheduling controller. An optimal combined method, i.e., Taguchi-hierarchical-genetic-algorithm (Taguchi-HGA), is presented in this paper to search for the optimal control parameters of both the grey predictor and the fuzzy gain scheduling controller (i.e., the sample size and grey constants of the grey predictor, the centers of the fuzzy regions, the left spread and the right spread of the membership functions, and the weighting matrices in the performance index of the linear quadratic regulator method) for guaranteeing stability and obtaining an optimal control performance. Computer simulations of a two-link robot arm example are performed to verify the effectiveness of the optimal GFGS control scheme designed by the Taguchi-HGA and to show that the optimal GFGS control scheme is superior to the existing optimal FGS (fuzzy-gain-scheduling) control scheme. Membership Function Autonomous System Combine Method Weighting Matrice Reference Trajectory Chou, Jyh-Horng aut Wu, Ying-Jeng aut Enthalten in Journal of intelligent & robotic systems Kluwer Academic Publishers, 1988 32(2001), 3 vom: Nov., Seite 321-345 (DE-627)130464864 (DE-600)740594-7 (DE-576)018667805 0921-0296 nnns volume:32 year:2001 number:3 month:11 pages:321-345 https://doi.org/10.1023/A:1013911905035 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_20 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2057 GBV_ILN_2244 GBV_ILN_4046 GBV_ILN_4266 GBV_ILN_4307 GBV_ILN_4318 AR 32 2001 3 11 321-345 |
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10.1023/A:1013911905035 doi (DE-627)OLC2057167194 (DE-He213)A:1013911905035-p DE-627 ger DE-627 rakwb eng 004 VZ Hsieh, Chen-Huei verfasserin aut Optimal Grey-Fuzzy Gain-Scheduler Design Using Taguchi-HGA Method 2001 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Kluwer Academic Publishers 2001 Abstract In this paper, the grey-fuzzy-gain-scheduling (GFGS) control scheme is proposed for making a nonlinear autonomous system to track a reference trajectory. The GFGS control scheme consists of two parts: the grey predictor and the fuzzy gain scheduling controller. An optimal combined method, i.e., Taguchi-hierarchical-genetic-algorithm (Taguchi-HGA), is presented in this paper to search for the optimal control parameters of both the grey predictor and the fuzzy gain scheduling controller (i.e., the sample size and grey constants of the grey predictor, the centers of the fuzzy regions, the left spread and the right spread of the membership functions, and the weighting matrices in the performance index of the linear quadratic regulator method) for guaranteeing stability and obtaining an optimal control performance. Computer simulations of a two-link robot arm example are performed to verify the effectiveness of the optimal GFGS control scheme designed by the Taguchi-HGA and to show that the optimal GFGS control scheme is superior to the existing optimal FGS (fuzzy-gain-scheduling) control scheme. Membership Function Autonomous System Combine Method Weighting Matrice Reference Trajectory Chou, Jyh-Horng aut Wu, Ying-Jeng aut Enthalten in Journal of intelligent & robotic systems Kluwer Academic Publishers, 1988 32(2001), 3 vom: Nov., Seite 321-345 (DE-627)130464864 (DE-600)740594-7 (DE-576)018667805 0921-0296 nnns volume:32 year:2001 number:3 month:11 pages:321-345 https://doi.org/10.1023/A:1013911905035 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_20 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2057 GBV_ILN_2244 GBV_ILN_4046 GBV_ILN_4266 GBV_ILN_4307 GBV_ILN_4318 AR 32 2001 3 11 321-345 |
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10.1023/A:1013911905035 doi (DE-627)OLC2057167194 (DE-He213)A:1013911905035-p DE-627 ger DE-627 rakwb eng 004 VZ Hsieh, Chen-Huei verfasserin aut Optimal Grey-Fuzzy Gain-Scheduler Design Using Taguchi-HGA Method 2001 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Kluwer Academic Publishers 2001 Abstract In this paper, the grey-fuzzy-gain-scheduling (GFGS) control scheme is proposed for making a nonlinear autonomous system to track a reference trajectory. The GFGS control scheme consists of two parts: the grey predictor and the fuzzy gain scheduling controller. An optimal combined method, i.e., Taguchi-hierarchical-genetic-algorithm (Taguchi-HGA), is presented in this paper to search for the optimal control parameters of both the grey predictor and the fuzzy gain scheduling controller (i.e., the sample size and grey constants of the grey predictor, the centers of the fuzzy regions, the left spread and the right spread of the membership functions, and the weighting matrices in the performance index of the linear quadratic regulator method) for guaranteeing stability and obtaining an optimal control performance. Computer simulations of a two-link robot arm example are performed to verify the effectiveness of the optimal GFGS control scheme designed by the Taguchi-HGA and to show that the optimal GFGS control scheme is superior to the existing optimal FGS (fuzzy-gain-scheduling) control scheme. Membership Function Autonomous System Combine Method Weighting Matrice Reference Trajectory Chou, Jyh-Horng aut Wu, Ying-Jeng aut Enthalten in Journal of intelligent & robotic systems Kluwer Academic Publishers, 1988 32(2001), 3 vom: Nov., Seite 321-345 (DE-627)130464864 (DE-600)740594-7 (DE-576)018667805 0921-0296 nnns volume:32 year:2001 number:3 month:11 pages:321-345 https://doi.org/10.1023/A:1013911905035 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_20 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2057 GBV_ILN_2244 GBV_ILN_4046 GBV_ILN_4266 GBV_ILN_4307 GBV_ILN_4318 AR 32 2001 3 11 321-345 |
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Optimal Grey-Fuzzy Gain-Scheduler Design Using Taguchi-HGA Method |
| abstract |
Abstract In this paper, the grey-fuzzy-gain-scheduling (GFGS) control scheme is proposed for making a nonlinear autonomous system to track a reference trajectory. The GFGS control scheme consists of two parts: the grey predictor and the fuzzy gain scheduling controller. An optimal combined method, i.e., Taguchi-hierarchical-genetic-algorithm (Taguchi-HGA), is presented in this paper to search for the optimal control parameters of both the grey predictor and the fuzzy gain scheduling controller (i.e., the sample size and grey constants of the grey predictor, the centers of the fuzzy regions, the left spread and the right spread of the membership functions, and the weighting matrices in the performance index of the linear quadratic regulator method) for guaranteeing stability and obtaining an optimal control performance. Computer simulations of a two-link robot arm example are performed to verify the effectiveness of the optimal GFGS control scheme designed by the Taguchi-HGA and to show that the optimal GFGS control scheme is superior to the existing optimal FGS (fuzzy-gain-scheduling) control scheme. © Kluwer Academic Publishers 2001 |
| abstractGer |
Abstract In this paper, the grey-fuzzy-gain-scheduling (GFGS) control scheme is proposed for making a nonlinear autonomous system to track a reference trajectory. The GFGS control scheme consists of two parts: the grey predictor and the fuzzy gain scheduling controller. An optimal combined method, i.e., Taguchi-hierarchical-genetic-algorithm (Taguchi-HGA), is presented in this paper to search for the optimal control parameters of both the grey predictor and the fuzzy gain scheduling controller (i.e., the sample size and grey constants of the grey predictor, the centers of the fuzzy regions, the left spread and the right spread of the membership functions, and the weighting matrices in the performance index of the linear quadratic regulator method) for guaranteeing stability and obtaining an optimal control performance. Computer simulations of a two-link robot arm example are performed to verify the effectiveness of the optimal GFGS control scheme designed by the Taguchi-HGA and to show that the optimal GFGS control scheme is superior to the existing optimal FGS (fuzzy-gain-scheduling) control scheme. © Kluwer Academic Publishers 2001 |
| abstract_unstemmed |
Abstract In this paper, the grey-fuzzy-gain-scheduling (GFGS) control scheme is proposed for making a nonlinear autonomous system to track a reference trajectory. The GFGS control scheme consists of two parts: the grey predictor and the fuzzy gain scheduling controller. An optimal combined method, i.e., Taguchi-hierarchical-genetic-algorithm (Taguchi-HGA), is presented in this paper to search for the optimal control parameters of both the grey predictor and the fuzzy gain scheduling controller (i.e., the sample size and grey constants of the grey predictor, the centers of the fuzzy regions, the left spread and the right spread of the membership functions, and the weighting matrices in the performance index of the linear quadratic regulator method) for guaranteeing stability and obtaining an optimal control performance. Computer simulations of a two-link robot arm example are performed to verify the effectiveness of the optimal GFGS control scheme designed by the Taguchi-HGA and to show that the optimal GFGS control scheme is superior to the existing optimal FGS (fuzzy-gain-scheduling) control scheme. © Kluwer Academic Publishers 2001 |
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| container_issue |
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| title_short |
Optimal Grey-Fuzzy Gain-Scheduler Design Using Taguchi-HGA Method |
| url |
https://doi.org/10.1023/A:1013911905035 |
| remote_bool |
false |
| author2 |
Chou, Jyh-Horng Wu, Ying-Jeng |
| author2Str |
Chou, Jyh-Horng Wu, Ying-Jeng |
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| doi_str |
10.1023/A:1013911905035 |
| up_date |
2024-07-03T14:07:05.256Z |
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1803567104268435456 |
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