Stable Neuro-Adaptive Control for Robots with the Upper Bound Estimation on the Neural Approximation Errors
Abstract An indirect adaptive control approach is developed in this paper for robots with unknown nonlinear dynamics using neural networks (NNs). A key property of the proposed approach is that the actual joint angle values in the control law are replaced by the desired joint angles, angle velocitie...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Sun, Fuchun [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
1999 |
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| Anmerkung: |
© Kluwer Academic Publishers 1999 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of intelligent & robotic systems - Kluwer Academic Publishers, 1988, 26(1999), 1 vom: Sept., Seite 91-100 |
|---|---|
| Übergeordnetes Werk: |
volume:26 ; year:1999 ; number:1 ; month:09 ; pages:91-100 |
| Links: |
|---|
| DOI / URN: |
10.1023/A:1008195720685 |
|---|
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OLC2057165485 |
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| 520 | |a Abstract An indirect adaptive control approach is developed in this paper for robots with unknown nonlinear dynamics using neural networks (NNs). A key property of the proposed approach is that the actual joint angle values in the control law are replaced by the desired joint angles, angle velocities and accelerators, and the bound on the NN reconstruction errors is assumed to be unknown. Main theoretical results for designing such a neuro-controller are given, and the control performance of the proposed controller is verified with simulation studies. | ||
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10.1023/A:1008195720685 doi (DE-627)OLC2057165485 (DE-He213)A:1008195720685-p DE-627 ger DE-627 rakwb eng 004 VZ Sun, Fuchun verfasserin aut Stable Neuro-Adaptive Control for Robots with the Upper Bound Estimation on the Neural Approximation Errors 1999 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Kluwer Academic Publishers 1999 Abstract An indirect adaptive control approach is developed in this paper for robots with unknown nonlinear dynamics using neural networks (NNs). A key property of the proposed approach is that the actual joint angle values in the control law are replaced by the desired joint angles, angle velocities and accelerators, and the bound on the NN reconstruction errors is assumed to be unknown. Main theoretical results for designing such a neuro-controller are given, and the control performance of the proposed controller is verified with simulation studies. Sun, Zengqi aut Zhu, Yunyue aut Lu, Wenjuan aut Enthalten in Journal of intelligent & robotic systems Kluwer Academic Publishers, 1988 26(1999), 1 vom: Sept., Seite 91-100 (DE-627)130464864 (DE-600)740594-7 (DE-576)018667805 0921-0296 nnns volume:26 year:1999 number:1 month:09 pages:91-100 https://doi.org/10.1023/A:1008195720685 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_20 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2020 GBV_ILN_2057 GBV_ILN_2241 GBV_ILN_2244 GBV_ILN_4046 GBV_ILN_4266 GBV_ILN_4307 GBV_ILN_4318 AR 26 1999 1 09 91-100 |
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10.1023/A:1008195720685 doi (DE-627)OLC2057165485 (DE-He213)A:1008195720685-p DE-627 ger DE-627 rakwb eng 004 VZ Sun, Fuchun verfasserin aut Stable Neuro-Adaptive Control for Robots with the Upper Bound Estimation on the Neural Approximation Errors 1999 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Kluwer Academic Publishers 1999 Abstract An indirect adaptive control approach is developed in this paper for robots with unknown nonlinear dynamics using neural networks (NNs). A key property of the proposed approach is that the actual joint angle values in the control law are replaced by the desired joint angles, angle velocities and accelerators, and the bound on the NN reconstruction errors is assumed to be unknown. Main theoretical results for designing such a neuro-controller are given, and the control performance of the proposed controller is verified with simulation studies. Sun, Zengqi aut Zhu, Yunyue aut Lu, Wenjuan aut Enthalten in Journal of intelligent & robotic systems Kluwer Academic Publishers, 1988 26(1999), 1 vom: Sept., Seite 91-100 (DE-627)130464864 (DE-600)740594-7 (DE-576)018667805 0921-0296 nnns volume:26 year:1999 number:1 month:09 pages:91-100 https://doi.org/10.1023/A:1008195720685 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_20 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2020 GBV_ILN_2057 GBV_ILN_2241 GBV_ILN_2244 GBV_ILN_4046 GBV_ILN_4266 GBV_ILN_4307 GBV_ILN_4318 AR 26 1999 1 09 91-100 |
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10.1023/A:1008195720685 doi (DE-627)OLC2057165485 (DE-He213)A:1008195720685-p DE-627 ger DE-627 rakwb eng 004 VZ Sun, Fuchun verfasserin aut Stable Neuro-Adaptive Control for Robots with the Upper Bound Estimation on the Neural Approximation Errors 1999 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Kluwer Academic Publishers 1999 Abstract An indirect adaptive control approach is developed in this paper for robots with unknown nonlinear dynamics using neural networks (NNs). A key property of the proposed approach is that the actual joint angle values in the control law are replaced by the desired joint angles, angle velocities and accelerators, and the bound on the NN reconstruction errors is assumed to be unknown. Main theoretical results for designing such a neuro-controller are given, and the control performance of the proposed controller is verified with simulation studies. Sun, Zengqi aut Zhu, Yunyue aut Lu, Wenjuan aut Enthalten in Journal of intelligent & robotic systems Kluwer Academic Publishers, 1988 26(1999), 1 vom: Sept., Seite 91-100 (DE-627)130464864 (DE-600)740594-7 (DE-576)018667805 0921-0296 nnns volume:26 year:1999 number:1 month:09 pages:91-100 https://doi.org/10.1023/A:1008195720685 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_20 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2020 GBV_ILN_2057 GBV_ILN_2241 GBV_ILN_2244 GBV_ILN_4046 GBV_ILN_4266 GBV_ILN_4307 GBV_ILN_4318 AR 26 1999 1 09 91-100 |
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Abstract An indirect adaptive control approach is developed in this paper for robots with unknown nonlinear dynamics using neural networks (NNs). A key property of the proposed approach is that the actual joint angle values in the control law are replaced by the desired joint angles, angle velocities and accelerators, and the bound on the NN reconstruction errors is assumed to be unknown. Main theoretical results for designing such a neuro-controller are given, and the control performance of the proposed controller is verified with simulation studies. © Kluwer Academic Publishers 1999 |
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Abstract An indirect adaptive control approach is developed in this paper for robots with unknown nonlinear dynamics using neural networks (NNs). A key property of the proposed approach is that the actual joint angle values in the control law are replaced by the desired joint angles, angle velocities and accelerators, and the bound on the NN reconstruction errors is assumed to be unknown. Main theoretical results for designing such a neuro-controller are given, and the control performance of the proposed controller is verified with simulation studies. © Kluwer Academic Publishers 1999 |
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A key property of the proposed approach is that the actual joint angle values in the control law are replaced by the desired joint angles, angle velocities and accelerators, and the bound on the NN reconstruction errors is assumed to be unknown. Main theoretical results for designing such a neuro-controller are given, and the control performance of the proposed controller is verified with simulation studies.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sun, Zengqi</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhu, Yunyue</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lu, Wenjuan</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Journal of intelligent & robotic systems</subfield><subfield code="d">Kluwer Academic Publishers, 1988</subfield><subfield code="g">26(1999), 1 vom: Sept., Seite 91-100</subfield><subfield code="w">(DE-627)130464864</subfield><subfield code="w">(DE-600)740594-7</subfield><subfield code="w">(DE-576)018667805</subfield><subfield code="x">0921-0296</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:26</subfield><subfield code="g">year:1999</subfield><subfield code="g">number:1</subfield><subfield code="g">month:09</subfield><subfield code="g">pages:91-100</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1023/A:1008195720685</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2057</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2241</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2244</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4266</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4318</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">26</subfield><subfield code="j">1999</subfield><subfield code="e">1</subfield><subfield code="c">09</subfield><subfield code="h">91-100</subfield></datafield></record></collection>
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