On near optimal neural control of multiple-input nonlinear systems
Abstract It has been a common consensus that general techniques for stabilization of nonlinear systems are available only for some special classes of nonlinear systems. Control design for nonlinear systems with uncertain components is usually carried out on a per system basis, especially when physic...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Chen, Dingguo [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2007 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag London Limited 2007 |
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| Übergeordnetes Werk: |
Enthalten in: Neural computing & applications - Springer-Verlag, 1993, 17(2007), 4 vom: 30. Mai, Seite 327-337 |
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| Übergeordnetes Werk: |
volume:17 ; year:2007 ; number:4 ; day:30 ; month:05 ; pages:327-337 |
| Links: |
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| DOI / URN: |
10.1007/s00521-007-0126-6 |
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OLC2025581033 |
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| 520 | |a Abstract It has been a common consensus that general techniques for stabilization of nonlinear systems are available only for some special classes of nonlinear systems. Control design for nonlinear systems with uncertain components is usually carried out on a per system basis, especially when physical control constraints, and certain control performance measures such as optimum time control are imposed. Elegant adaptive control techniques are difficult to apply to this type of problems. A new neural network based control design is proposed and presented in this paper to deal with a special class of uncertain nonlinear systems with multiple inputs. The desired system dynamics are analyzed and utilized in the process of the proposed intelligent control design. The theoretical results are provided to justify the design procedures. The simulation study is conducted on a second-order bilinear system with two inputs and uncertainties on its parameters. The simulation results indicate that the proposed design approach is effective. | ||
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| 700 | 1 | |a Mohler, Ronald R. |4 aut | |
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10.1007/s00521-007-0126-6 doi (DE-627)OLC2025581033 (DE-He213)s00521-007-0126-6-p DE-627 ger DE-627 rakwb eng 004 VZ Chen, Dingguo verfasserin aut On near optimal neural control of multiple-input nonlinear systems 2007 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Limited 2007 Abstract It has been a common consensus that general techniques for stabilization of nonlinear systems are available only for some special classes of nonlinear systems. Control design for nonlinear systems with uncertain components is usually carried out on a per system basis, especially when physical control constraints, and certain control performance measures such as optimum time control are imposed. Elegant adaptive control techniques are difficult to apply to this type of problems. A new neural network based control design is proposed and presented in this paper to deal with a special class of uncertain nonlinear systems with multiple inputs. The desired system dynamics are analyzed and utilized in the process of the proposed intelligent control design. The theoretical results are provided to justify the design procedures. The simulation study is conducted on a second-order bilinear system with two inputs and uncertainties on its parameters. The simulation results indicate that the proposed design approach is effective. Uncertain nonlinear system Multiple input nonlinear system Neural network Optimal control Neural control Switching manifold Yang, Jiaben aut Mohler, Ronald R. aut Enthalten in Neural computing & applications Springer-Verlag, 1993 17(2007), 4 vom: 30. Mai, Seite 327-337 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:17 year:2007 number:4 day:30 month:05 pages:327-337 https://doi.org/10.1007/s00521-007-0126-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_100 GBV_ILN_152 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_4046 GBV_ILN_4277 AR 17 2007 4 30 05 327-337 |
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10.1007/s00521-007-0126-6 doi (DE-627)OLC2025581033 (DE-He213)s00521-007-0126-6-p DE-627 ger DE-627 rakwb eng 004 VZ Chen, Dingguo verfasserin aut On near optimal neural control of multiple-input nonlinear systems 2007 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Limited 2007 Abstract It has been a common consensus that general techniques for stabilization of nonlinear systems are available only for some special classes of nonlinear systems. Control design for nonlinear systems with uncertain components is usually carried out on a per system basis, especially when physical control constraints, and certain control performance measures such as optimum time control are imposed. Elegant adaptive control techniques are difficult to apply to this type of problems. A new neural network based control design is proposed and presented in this paper to deal with a special class of uncertain nonlinear systems with multiple inputs. The desired system dynamics are analyzed and utilized in the process of the proposed intelligent control design. The theoretical results are provided to justify the design procedures. The simulation study is conducted on a second-order bilinear system with two inputs and uncertainties on its parameters. The simulation results indicate that the proposed design approach is effective. Uncertain nonlinear system Multiple input nonlinear system Neural network Optimal control Neural control Switching manifold Yang, Jiaben aut Mohler, Ronald R. aut Enthalten in Neural computing & applications Springer-Verlag, 1993 17(2007), 4 vom: 30. Mai, Seite 327-337 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:17 year:2007 number:4 day:30 month:05 pages:327-337 https://doi.org/10.1007/s00521-007-0126-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_100 GBV_ILN_152 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_4046 GBV_ILN_4277 AR 17 2007 4 30 05 327-337 |
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10.1007/s00521-007-0126-6 doi (DE-627)OLC2025581033 (DE-He213)s00521-007-0126-6-p DE-627 ger DE-627 rakwb eng 004 VZ Chen, Dingguo verfasserin aut On near optimal neural control of multiple-input nonlinear systems 2007 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Limited 2007 Abstract It has been a common consensus that general techniques for stabilization of nonlinear systems are available only for some special classes of nonlinear systems. Control design for nonlinear systems with uncertain components is usually carried out on a per system basis, especially when physical control constraints, and certain control performance measures such as optimum time control are imposed. Elegant adaptive control techniques are difficult to apply to this type of problems. A new neural network based control design is proposed and presented in this paper to deal with a special class of uncertain nonlinear systems with multiple inputs. The desired system dynamics are analyzed and utilized in the process of the proposed intelligent control design. The theoretical results are provided to justify the design procedures. The simulation study is conducted on a second-order bilinear system with two inputs and uncertainties on its parameters. The simulation results indicate that the proposed design approach is effective. Uncertain nonlinear system Multiple input nonlinear system Neural network Optimal control Neural control Switching manifold Yang, Jiaben aut Mohler, Ronald R. aut Enthalten in Neural computing & applications Springer-Verlag, 1993 17(2007), 4 vom: 30. Mai, Seite 327-337 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:17 year:2007 number:4 day:30 month:05 pages:327-337 https://doi.org/10.1007/s00521-007-0126-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_100 GBV_ILN_152 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_4046 GBV_ILN_4277 AR 17 2007 4 30 05 327-337 |
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10.1007/s00521-007-0126-6 doi (DE-627)OLC2025581033 (DE-He213)s00521-007-0126-6-p DE-627 ger DE-627 rakwb eng 004 VZ Chen, Dingguo verfasserin aut On near optimal neural control of multiple-input nonlinear systems 2007 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Limited 2007 Abstract It has been a common consensus that general techniques for stabilization of nonlinear systems are available only for some special classes of nonlinear systems. Control design for nonlinear systems with uncertain components is usually carried out on a per system basis, especially when physical control constraints, and certain control performance measures such as optimum time control are imposed. Elegant adaptive control techniques are difficult to apply to this type of problems. A new neural network based control design is proposed and presented in this paper to deal with a special class of uncertain nonlinear systems with multiple inputs. The desired system dynamics are analyzed and utilized in the process of the proposed intelligent control design. The theoretical results are provided to justify the design procedures. The simulation study is conducted on a second-order bilinear system with two inputs and uncertainties on its parameters. The simulation results indicate that the proposed design approach is effective. Uncertain nonlinear system Multiple input nonlinear system Neural network Optimal control Neural control Switching manifold Yang, Jiaben aut Mohler, Ronald R. aut Enthalten in Neural computing & applications Springer-Verlag, 1993 17(2007), 4 vom: 30. Mai, Seite 327-337 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:17 year:2007 number:4 day:30 month:05 pages:327-337 https://doi.org/10.1007/s00521-007-0126-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_100 GBV_ILN_152 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_4046 GBV_ILN_4277 AR 17 2007 4 30 05 327-337 |
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10.1007/s00521-007-0126-6 doi (DE-627)OLC2025581033 (DE-He213)s00521-007-0126-6-p DE-627 ger DE-627 rakwb eng 004 VZ Chen, Dingguo verfasserin aut On near optimal neural control of multiple-input nonlinear systems 2007 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Limited 2007 Abstract It has been a common consensus that general techniques for stabilization of nonlinear systems are available only for some special classes of nonlinear systems. Control design for nonlinear systems with uncertain components is usually carried out on a per system basis, especially when physical control constraints, and certain control performance measures such as optimum time control are imposed. Elegant adaptive control techniques are difficult to apply to this type of problems. A new neural network based control design is proposed and presented in this paper to deal with a special class of uncertain nonlinear systems with multiple inputs. The desired system dynamics are analyzed and utilized in the process of the proposed intelligent control design. The theoretical results are provided to justify the design procedures. The simulation study is conducted on a second-order bilinear system with two inputs and uncertainties on its parameters. The simulation results indicate that the proposed design approach is effective. Uncertain nonlinear system Multiple input nonlinear system Neural network Optimal control Neural control Switching manifold Yang, Jiaben aut Mohler, Ronald R. aut Enthalten in Neural computing & applications Springer-Verlag, 1993 17(2007), 4 vom: 30. Mai, Seite 327-337 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:17 year:2007 number:4 day:30 month:05 pages:327-337 https://doi.org/10.1007/s00521-007-0126-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_100 GBV_ILN_152 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_4046 GBV_ILN_4277 AR 17 2007 4 30 05 327-337 |
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Abstract It has been a common consensus that general techniques for stabilization of nonlinear systems are available only for some special classes of nonlinear systems. Control design for nonlinear systems with uncertain components is usually carried out on a per system basis, especially when physical control constraints, and certain control performance measures such as optimum time control are imposed. Elegant adaptive control techniques are difficult to apply to this type of problems. A new neural network based control design is proposed and presented in this paper to deal with a special class of uncertain nonlinear systems with multiple inputs. The desired system dynamics are analyzed and utilized in the process of the proposed intelligent control design. The theoretical results are provided to justify the design procedures. The simulation study is conducted on a second-order bilinear system with two inputs and uncertainties on its parameters. The simulation results indicate that the proposed design approach is effective. © Springer-Verlag London Limited 2007 |
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Abstract It has been a common consensus that general techniques for stabilization of nonlinear systems are available only for some special classes of nonlinear systems. Control design for nonlinear systems with uncertain components is usually carried out on a per system basis, especially when physical control constraints, and certain control performance measures such as optimum time control are imposed. Elegant adaptive control techniques are difficult to apply to this type of problems. A new neural network based control design is proposed and presented in this paper to deal with a special class of uncertain nonlinear systems with multiple inputs. The desired system dynamics are analyzed and utilized in the process of the proposed intelligent control design. The theoretical results are provided to justify the design procedures. The simulation study is conducted on a second-order bilinear system with two inputs and uncertainties on its parameters. The simulation results indicate that the proposed design approach is effective. © Springer-Verlag London Limited 2007 |
| abstract_unstemmed |
Abstract It has been a common consensus that general techniques for stabilization of nonlinear systems are available only for some special classes of nonlinear systems. Control design for nonlinear systems with uncertain components is usually carried out on a per system basis, especially when physical control constraints, and certain control performance measures such as optimum time control are imposed. Elegant adaptive control techniques are difficult to apply to this type of problems. A new neural network based control design is proposed and presented in this paper to deal with a special class of uncertain nonlinear systems with multiple inputs. The desired system dynamics are analyzed and utilized in the process of the proposed intelligent control design. The theoretical results are provided to justify the design procedures. The simulation study is conducted on a second-order bilinear system with two inputs and uncertainties on its parameters. The simulation results indicate that the proposed design approach is effective. © Springer-Verlag London Limited 2007 |
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On near optimal neural control of multiple-input nonlinear systems |
| url |
https://doi.org/10.1007/s00521-007-0126-6 |
| remote_bool |
false |
| author2 |
Yang, Jiaben Mohler, Ronald R. |
| author2Str |
Yang, Jiaben Mohler, Ronald R. |
| ppnlink |
165669608 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s00521-007-0126-6 |
| up_date |
2024-07-04T01:36:24.439Z |
| _version_ |
1803610472515108864 |
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7.3993244 |