Stable Sampled-data Adaptive Control of Robot Arms Using Neural Networks

Abstract Stable neural network-based sampled-data indirect and direct adaptivecontrol approaches, which are the integration of a neural network (NN)approach and the adaptive implementation of the discrete variable structurecontrol, are developed in this paper for the trajectory tracking control ofa...
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Autor*in:	Sun, Fuchun [verfasserIn] Sun, Zengqi

Format:	Artikel
Sprache:	Englisch

Erschienen:	1997

Anmerkung:	© Kluwer Academic Publishers 1997

Übergeordnetes Werk:	Enthalten in: Journal of intelligent & robotic systems - Kluwer Academic Publishers, 1988, 20(1997), 2-4 vom: Sept., Seite 131-155
Übergeordnetes Werk:	volume:20 ; year:1997 ; number:2-4 ; month:09 ; pages:131-155

Links:	Volltext

DOI / URN:	10.1023/A:1007900125801

Katalog-ID:	OLC2057164284

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allfields	10.1023/A:1007900125801 doi (DE-627)OLC2057164284 (DE-He213)A:1007900125801-p DE-627 ger DE-627 rakwb eng 004 VZ Sun, Fuchun verfasserin aut Stable Sampled-data Adaptive Control of Robot Arms Using Neural Networks 1997 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Kluwer Academic Publishers 1997 Abstract Stable neural network-based sampled-data indirect and direct adaptivecontrol approaches, which are the integration of a neural network (NN)approach and the adaptive implementation of the discrete variable structurecontrol, are developed in this paper for the trajectory tracking control ofa robot arm with unknown nonlinear dynamics. The robot arm is assumed tohave an upper and lower bound of its inertia matrix norm and its states areavailable for measurement. The discrete variable structure control servestwo purposes, i.e., one is to force the system states to be within the stateregion in which neural networks are used when the system goes out of neuralcontrol; and the other is to improve the tracking performance within the NNapproximation region. Main theory results for designing stable neuralnetwork-based sampled data indirect and direct adaptive controllers aregiven, and the extension of the proposed control approaches to the compositeadaptive control of a flexible-link robot is discussed. Finally, theeffectiveness of the proposed control approaches is illustrated throughsimulation studies. Sun, Zengqi aut Enthalten in Journal of intelligent & robotic systems Kluwer Academic Publishers, 1988 20(1997), 2-4 vom: Sept., Seite 131-155 (DE-627)130464864 (DE-600)740594-7 (DE-576)018667805 0921-0296 nnns volume:20 year:1997 number:2-4 month:09 pages:131-155 https://doi.org/10.1023/A:1007900125801 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 20 1997 2-4 09 131-155
spelling	10.1023/A:1007900125801 doi (DE-627)OLC2057164284 (DE-He213)A:1007900125801-p DE-627 ger DE-627 rakwb eng 004 VZ Sun, Fuchun verfasserin aut Stable Sampled-data Adaptive Control of Robot Arms Using Neural Networks 1997 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Kluwer Academic Publishers 1997 Abstract Stable neural network-based sampled-data indirect and direct adaptivecontrol approaches, which are the integration of a neural network (NN)approach and the adaptive implementation of the discrete variable structurecontrol, are developed in this paper for the trajectory tracking control ofa robot arm with unknown nonlinear dynamics. The robot arm is assumed tohave an upper and lower bound of its inertia matrix norm and its states areavailable for measurement. The discrete variable structure control servestwo purposes, i.e., one is to force the system states to be within the stateregion in which neural networks are used when the system goes out of neuralcontrol; and the other is to improve the tracking performance within the NNapproximation region. Main theory results for designing stable neuralnetwork-based sampled data indirect and direct adaptive controllers aregiven, and the extension of the proposed control approaches to the compositeadaptive control of a flexible-link robot is discussed. Finally, theeffectiveness of the proposed control approaches is illustrated throughsimulation studies. Sun, Zengqi aut Enthalten in Journal of intelligent & robotic systems Kluwer Academic Publishers, 1988 20(1997), 2-4 vom: Sept., Seite 131-155 (DE-627)130464864 (DE-600)740594-7 (DE-576)018667805 0921-0296 nnns volume:20 year:1997 number:2-4 month:09 pages:131-155 https://doi.org/10.1023/A:1007900125801 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 20 1997 2-4 09 131-155
allfields_unstemmed	10.1023/A:1007900125801 doi (DE-627)OLC2057164284 (DE-He213)A:1007900125801-p DE-627 ger DE-627 rakwb eng 004 VZ Sun, Fuchun verfasserin aut Stable Sampled-data Adaptive Control of Robot Arms Using Neural Networks 1997 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Kluwer Academic Publishers 1997 Abstract Stable neural network-based sampled-data indirect and direct adaptivecontrol approaches, which are the integration of a neural network (NN)approach and the adaptive implementation of the discrete variable structurecontrol, are developed in this paper for the trajectory tracking control ofa robot arm with unknown nonlinear dynamics. The robot arm is assumed tohave an upper and lower bound of its inertia matrix norm and its states areavailable for measurement. The discrete variable structure control servestwo purposes, i.e., one is to force the system states to be within the stateregion in which neural networks are used when the system goes out of neuralcontrol; and the other is to improve the tracking performance within the NNapproximation region. Main theory results for designing stable neuralnetwork-based sampled data indirect and direct adaptive controllers aregiven, and the extension of the proposed control approaches to the compositeadaptive control of a flexible-link robot is discussed. Finally, theeffectiveness of the proposed control approaches is illustrated throughsimulation studies. Sun, Zengqi aut Enthalten in Journal of intelligent & robotic systems Kluwer Academic Publishers, 1988 20(1997), 2-4 vom: Sept., Seite 131-155 (DE-627)130464864 (DE-600)740594-7 (DE-576)018667805 0921-0296 nnns volume:20 year:1997 number:2-4 month:09 pages:131-155 https://doi.org/10.1023/A:1007900125801 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 20 1997 2-4 09 131-155
allfieldsGer	10.1023/A:1007900125801 doi (DE-627)OLC2057164284 (DE-He213)A:1007900125801-p DE-627 ger DE-627 rakwb eng 004 VZ Sun, Fuchun verfasserin aut Stable Sampled-data Adaptive Control of Robot Arms Using Neural Networks 1997 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Kluwer Academic Publishers 1997 Abstract Stable neural network-based sampled-data indirect and direct adaptivecontrol approaches, which are the integration of a neural network (NN)approach and the adaptive implementation of the discrete variable structurecontrol, are developed in this paper for the trajectory tracking control ofa robot arm with unknown nonlinear dynamics. The robot arm is assumed tohave an upper and lower bound of its inertia matrix norm and its states areavailable for measurement. The discrete variable structure control servestwo purposes, i.e., one is to force the system states to be within the stateregion in which neural networks are used when the system goes out of neuralcontrol; and the other is to improve the tracking performance within the NNapproximation region. Main theory results for designing stable neuralnetwork-based sampled data indirect and direct adaptive controllers aregiven, and the extension of the proposed control approaches to the compositeadaptive control of a flexible-link robot is discussed. Finally, theeffectiveness of the proposed control approaches is illustrated throughsimulation studies. Sun, Zengqi aut Enthalten in Journal of intelligent & robotic systems Kluwer Academic Publishers, 1988 20(1997), 2-4 vom: Sept., Seite 131-155 (DE-627)130464864 (DE-600)740594-7 (DE-576)018667805 0921-0296 nnns volume:20 year:1997 number:2-4 month:09 pages:131-155 https://doi.org/10.1023/A:1007900125801 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 20 1997 2-4 09 131-155
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title_auth	Stable Sampled-data Adaptive Control of Robot Arms Using Neural Networks
abstract	Abstract Stable neural network-based sampled-data indirect and direct adaptivecontrol approaches, which are the integration of a neural network (NN)approach and the adaptive implementation of the discrete variable structurecontrol, are developed in this paper for the trajectory tracking control ofa robot arm with unknown nonlinear dynamics. The robot arm is assumed tohave an upper and lower bound of its inertia matrix norm and its states areavailable for measurement. The discrete variable structure control servestwo purposes, i.e., one is to force the system states to be within the stateregion in which neural networks are used when the system goes out of neuralcontrol; and the other is to improve the tracking performance within the NNapproximation region. Main theory results for designing stable neuralnetwork-based sampled data indirect and direct adaptive controllers aregiven, and the extension of the proposed control approaches to the compositeadaptive control of a flexible-link robot is discussed. Finally, theeffectiveness of the proposed control approaches is illustrated throughsimulation studies. © Kluwer Academic Publishers 1997
abstractGer	Abstract Stable neural network-based sampled-data indirect and direct adaptivecontrol approaches, which are the integration of a neural network (NN)approach and the adaptive implementation of the discrete variable structurecontrol, are developed in this paper for the trajectory tracking control ofa robot arm with unknown nonlinear dynamics. The robot arm is assumed tohave an upper and lower bound of its inertia matrix norm and its states areavailable for measurement. The discrete variable structure control servestwo purposes, i.e., one is to force the system states to be within the stateregion in which neural networks are used when the system goes out of neuralcontrol; and the other is to improve the tracking performance within the NNapproximation region. Main theory results for designing stable neuralnetwork-based sampled data indirect and direct adaptive controllers aregiven, and the extension of the proposed control approaches to the compositeadaptive control of a flexible-link robot is discussed. Finally, theeffectiveness of the proposed control approaches is illustrated throughsimulation studies. © Kluwer Academic Publishers 1997
abstract_unstemmed	Abstract Stable neural network-based sampled-data indirect and direct adaptivecontrol approaches, which are the integration of a neural network (NN)approach and the adaptive implementation of the discrete variable structurecontrol, are developed in this paper for the trajectory tracking control ofa robot arm with unknown nonlinear dynamics. The robot arm is assumed tohave an upper and lower bound of its inertia matrix norm and its states areavailable for measurement. The discrete variable structure control servestwo purposes, i.e., one is to force the system states to be within the stateregion in which neural networks are used when the system goes out of neuralcontrol; and the other is to improve the tracking performance within the NNapproximation region. Main theory results for designing stable neuralnetwork-based sampled data indirect and direct adaptive controllers aregiven, and the extension of the proposed control approaches to the compositeadaptive control of a flexible-link robot is discussed. Finally, theeffectiveness of the proposed control approaches is illustrated throughsimulation studies. © Kluwer Academic Publishers 1997
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The robot arm is assumed tohave an upper and lower bound of its inertia matrix norm and its states areavailable for measurement. The discrete variable structure control servestwo purposes, i.e., one is to force the system states to be within the stateregion in which neural networks are used when the system goes out of neuralcontrol; and the other is to improve the tracking performance within the NNapproximation region. Main theory results for designing stable neuralnetwork-based sampled data indirect and direct adaptive controllers aregiven, and the extension of the proposed control approaches to the compositeadaptive control of a flexible-link robot is discussed. 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Stable Sampled-data Adaptive Control of Robot Arms Using Neural Networks

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