Model-free adaptive optimal design for trajectory tracking control of rocket-powered vehicle
An adaptive optimal trajectory tracking controller is presented for the Solid-Rocket-Powered Vehicle (SRPV) with uncertain nonlinear non-affine dynamics in the framework of adaptive dynamic programming. First, considering that the ascent model of the SRPV is non-affine, a model-free Single Network A...
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| Autor*in: |
NIE, Wenming [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020transfer abstract |
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| Umfang: |
14 |
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| Übergeordnetes Werk: |
Enthalten in: Computable convergence bounds of series expansions for infinite dimensional linear-analytic systems and application - Hélie, Thomas ELSEVIER, 2014transfer abstract, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:33 ; year:2020 ; number:6 ; pages:1703-1716 ; extent:14 |
| Links: |
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| DOI / URN: |
10.1016/j.cja.2020.02.022 |
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ELV050630733 |
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| 520 | |a An adaptive optimal trajectory tracking controller is presented for the Solid-Rocket-Powered Vehicle (SRPV) with uncertain nonlinear non-affine dynamics in the framework of adaptive dynamic programming. First, considering that the ascent model of the SRPV is non-affine, a model-free Single Network Adaptive Critic (SNAC) method is developed based on the dynamic neural network and the traditional SNAC method. This developed model-free SNAC method overcomes the limitation of the traditional SNAC method that can only be applied to affine systems. Then, a closed-form adaptive optimal controller is designed for the non-affine dynamics of SRPVs. This controller can adjust its parameters under different flight conditions and converge to the approximate optimal controller through online self-learning. Finally, the convergence to the approximate optimal controller is proved. The theoretical analysis of the uniformly ultimate boundedness of the tracking error is also presented. Simulation results demonstrate the effectiveness of the proposed controller. | ||
| 520 | |a An adaptive optimal trajectory tracking controller is presented for the Solid-Rocket-Powered Vehicle (SRPV) with uncertain nonlinear non-affine dynamics in the framework of adaptive dynamic programming. First, considering that the ascent model of the SRPV is non-affine, a model-free Single Network Adaptive Critic (SNAC) method is developed based on the dynamic neural network and the traditional SNAC method. This developed model-free SNAC method overcomes the limitation of the traditional SNAC method that can only be applied to affine systems. Then, a closed-form adaptive optimal controller is designed for the non-affine dynamics of SRPVs. This controller can adjust its parameters under different flight conditions and converge to the approximate optimal controller through online self-learning. Finally, the convergence to the approximate optimal controller is proved. The theoretical analysis of the uniformly ultimate boundedness of the tracking error is also presented. Simulation results demonstrate the effectiveness of the proposed controller. | ||
| 650 | 7 | |a Model-free |2 Elsevier | |
| 650 | 7 | |a Trajectory tracking |2 Elsevier | |
| 650 | 7 | |a Dynamic neural network |2 Elsevier | |
| 650 | 7 | |a Adaptive dynamic programming |2 Elsevier | |
| 650 | 7 | |a Solid-rocket-powered vehicle |2 Elsevier | |
| 700 | 1 | |a LI, Huifeng |4 oth | |
| 700 | 1 | |a ZHANG, Ran |4 oth | |
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10.1016/j.cja.2020.02.022 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001036.pica (DE-627)ELV050630733 (ELSEVIER)S1000-9361(20)30087-X DE-627 ger DE-627 rakwb eng 000 VZ 620 VZ 610 VZ 44.48 bkl NIE, Wenming verfasserin aut Model-free adaptive optimal design for trajectory tracking control of rocket-powered vehicle 2020transfer abstract 14 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier An adaptive optimal trajectory tracking controller is presented for the Solid-Rocket-Powered Vehicle (SRPV) with uncertain nonlinear non-affine dynamics in the framework of adaptive dynamic programming. First, considering that the ascent model of the SRPV is non-affine, a model-free Single Network Adaptive Critic (SNAC) method is developed based on the dynamic neural network and the traditional SNAC method. This developed model-free SNAC method overcomes the limitation of the traditional SNAC method that can only be applied to affine systems. Then, a closed-form adaptive optimal controller is designed for the non-affine dynamics of SRPVs. This controller can adjust its parameters under different flight conditions and converge to the approximate optimal controller through online self-learning. Finally, the convergence to the approximate optimal controller is proved. The theoretical analysis of the uniformly ultimate boundedness of the tracking error is also presented. Simulation results demonstrate the effectiveness of the proposed controller. An adaptive optimal trajectory tracking controller is presented for the Solid-Rocket-Powered Vehicle (SRPV) with uncertain nonlinear non-affine dynamics in the framework of adaptive dynamic programming. First, considering that the ascent model of the SRPV is non-affine, a model-free Single Network Adaptive Critic (SNAC) method is developed based on the dynamic neural network and the traditional SNAC method. This developed model-free SNAC method overcomes the limitation of the traditional SNAC method that can only be applied to affine systems. Then, a closed-form adaptive optimal controller is designed for the non-affine dynamics of SRPVs. This controller can adjust its parameters under different flight conditions and converge to the approximate optimal controller through online self-learning. Finally, the convergence to the approximate optimal controller is proved. The theoretical analysis of the uniformly ultimate boundedness of the tracking error is also presented. Simulation results demonstrate the effectiveness of the proposed controller. Model-free Elsevier Trajectory tracking Elsevier Dynamic neural network Elsevier Adaptive dynamic programming Elsevier Solid-rocket-powered vehicle Elsevier LI, Huifeng oth ZHANG, Ran oth Enthalten in Elsevier Hélie, Thomas ELSEVIER Computable convergence bounds of series expansions for infinite dimensional linear-analytic systems and application 2014transfer abstract Amsterdam [u.a.] (DE-627)ELV017935458 volume:33 year:2020 number:6 pages:1703-1716 extent:14 https://doi.org/10.1016/j.cja.2020.02.022 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_63 GBV_ILN_70 44.48 Medizinische Genetik VZ AR 33 2020 6 1703-1716 14 |
| spelling |
10.1016/j.cja.2020.02.022 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001036.pica (DE-627)ELV050630733 (ELSEVIER)S1000-9361(20)30087-X DE-627 ger DE-627 rakwb eng 000 VZ 620 VZ 610 VZ 44.48 bkl NIE, Wenming verfasserin aut Model-free adaptive optimal design for trajectory tracking control of rocket-powered vehicle 2020transfer abstract 14 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier An adaptive optimal trajectory tracking controller is presented for the Solid-Rocket-Powered Vehicle (SRPV) with uncertain nonlinear non-affine dynamics in the framework of adaptive dynamic programming. First, considering that the ascent model of the SRPV is non-affine, a model-free Single Network Adaptive Critic (SNAC) method is developed based on the dynamic neural network and the traditional SNAC method. This developed model-free SNAC method overcomes the limitation of the traditional SNAC method that can only be applied to affine systems. Then, a closed-form adaptive optimal controller is designed for the non-affine dynamics of SRPVs. This controller can adjust its parameters under different flight conditions and converge to the approximate optimal controller through online self-learning. Finally, the convergence to the approximate optimal controller is proved. The theoretical analysis of the uniformly ultimate boundedness of the tracking error is also presented. Simulation results demonstrate the effectiveness of the proposed controller. An adaptive optimal trajectory tracking controller is presented for the Solid-Rocket-Powered Vehicle (SRPV) with uncertain nonlinear non-affine dynamics in the framework of adaptive dynamic programming. First, considering that the ascent model of the SRPV is non-affine, a model-free Single Network Adaptive Critic (SNAC) method is developed based on the dynamic neural network and the traditional SNAC method. This developed model-free SNAC method overcomes the limitation of the traditional SNAC method that can only be applied to affine systems. Then, a closed-form adaptive optimal controller is designed for the non-affine dynamics of SRPVs. This controller can adjust its parameters under different flight conditions and converge to the approximate optimal controller through online self-learning. Finally, the convergence to the approximate optimal controller is proved. The theoretical analysis of the uniformly ultimate boundedness of the tracking error is also presented. Simulation results demonstrate the effectiveness of the proposed controller. Model-free Elsevier Trajectory tracking Elsevier Dynamic neural network Elsevier Adaptive dynamic programming Elsevier Solid-rocket-powered vehicle Elsevier LI, Huifeng oth ZHANG, Ran oth Enthalten in Elsevier Hélie, Thomas ELSEVIER Computable convergence bounds of series expansions for infinite dimensional linear-analytic systems and application 2014transfer abstract Amsterdam [u.a.] (DE-627)ELV017935458 volume:33 year:2020 number:6 pages:1703-1716 extent:14 https://doi.org/10.1016/j.cja.2020.02.022 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_63 GBV_ILN_70 44.48 Medizinische Genetik VZ AR 33 2020 6 1703-1716 14 |
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10.1016/j.cja.2020.02.022 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001036.pica (DE-627)ELV050630733 (ELSEVIER)S1000-9361(20)30087-X DE-627 ger DE-627 rakwb eng 000 VZ 620 VZ 610 VZ 44.48 bkl NIE, Wenming verfasserin aut Model-free adaptive optimal design for trajectory tracking control of rocket-powered vehicle 2020transfer abstract 14 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier An adaptive optimal trajectory tracking controller is presented for the Solid-Rocket-Powered Vehicle (SRPV) with uncertain nonlinear non-affine dynamics in the framework of adaptive dynamic programming. First, considering that the ascent model of the SRPV is non-affine, a model-free Single Network Adaptive Critic (SNAC) method is developed based on the dynamic neural network and the traditional SNAC method. This developed model-free SNAC method overcomes the limitation of the traditional SNAC method that can only be applied to affine systems. Then, a closed-form adaptive optimal controller is designed for the non-affine dynamics of SRPVs. This controller can adjust its parameters under different flight conditions and converge to the approximate optimal controller through online self-learning. Finally, the convergence to the approximate optimal controller is proved. The theoretical analysis of the uniformly ultimate boundedness of the tracking error is also presented. Simulation results demonstrate the effectiveness of the proposed controller. An adaptive optimal trajectory tracking controller is presented for the Solid-Rocket-Powered Vehicle (SRPV) with uncertain nonlinear non-affine dynamics in the framework of adaptive dynamic programming. First, considering that the ascent model of the SRPV is non-affine, a model-free Single Network Adaptive Critic (SNAC) method is developed based on the dynamic neural network and the traditional SNAC method. This developed model-free SNAC method overcomes the limitation of the traditional SNAC method that can only be applied to affine systems. Then, a closed-form adaptive optimal controller is designed for the non-affine dynamics of SRPVs. This controller can adjust its parameters under different flight conditions and converge to the approximate optimal controller through online self-learning. Finally, the convergence to the approximate optimal controller is proved. The theoretical analysis of the uniformly ultimate boundedness of the tracking error is also presented. Simulation results demonstrate the effectiveness of the proposed controller. Model-free Elsevier Trajectory tracking Elsevier Dynamic neural network Elsevier Adaptive dynamic programming Elsevier Solid-rocket-powered vehicle Elsevier LI, Huifeng oth ZHANG, Ran oth Enthalten in Elsevier Hélie, Thomas ELSEVIER Computable convergence bounds of series expansions for infinite dimensional linear-analytic systems and application 2014transfer abstract Amsterdam [u.a.] (DE-627)ELV017935458 volume:33 year:2020 number:6 pages:1703-1716 extent:14 https://doi.org/10.1016/j.cja.2020.02.022 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_63 GBV_ILN_70 44.48 Medizinische Genetik VZ AR 33 2020 6 1703-1716 14 |
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10.1016/j.cja.2020.02.022 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001036.pica (DE-627)ELV050630733 (ELSEVIER)S1000-9361(20)30087-X DE-627 ger DE-627 rakwb eng 000 VZ 620 VZ 610 VZ 44.48 bkl NIE, Wenming verfasserin aut Model-free adaptive optimal design for trajectory tracking control of rocket-powered vehicle 2020transfer abstract 14 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier An adaptive optimal trajectory tracking controller is presented for the Solid-Rocket-Powered Vehicle (SRPV) with uncertain nonlinear non-affine dynamics in the framework of adaptive dynamic programming. First, considering that the ascent model of the SRPV is non-affine, a model-free Single Network Adaptive Critic (SNAC) method is developed based on the dynamic neural network and the traditional SNAC method. This developed model-free SNAC method overcomes the limitation of the traditional SNAC method that can only be applied to affine systems. Then, a closed-form adaptive optimal controller is designed for the non-affine dynamics of SRPVs. This controller can adjust its parameters under different flight conditions and converge to the approximate optimal controller through online self-learning. Finally, the convergence to the approximate optimal controller is proved. The theoretical analysis of the uniformly ultimate boundedness of the tracking error is also presented. Simulation results demonstrate the effectiveness of the proposed controller. An adaptive optimal trajectory tracking controller is presented for the Solid-Rocket-Powered Vehicle (SRPV) with uncertain nonlinear non-affine dynamics in the framework of adaptive dynamic programming. First, considering that the ascent model of the SRPV is non-affine, a model-free Single Network Adaptive Critic (SNAC) method is developed based on the dynamic neural network and the traditional SNAC method. This developed model-free SNAC method overcomes the limitation of the traditional SNAC method that can only be applied to affine systems. Then, a closed-form adaptive optimal controller is designed for the non-affine dynamics of SRPVs. This controller can adjust its parameters under different flight conditions and converge to the approximate optimal controller through online self-learning. Finally, the convergence to the approximate optimal controller is proved. The theoretical analysis of the uniformly ultimate boundedness of the tracking error is also presented. Simulation results demonstrate the effectiveness of the proposed controller. Model-free Elsevier Trajectory tracking Elsevier Dynamic neural network Elsevier Adaptive dynamic programming Elsevier Solid-rocket-powered vehicle Elsevier LI, Huifeng oth ZHANG, Ran oth Enthalten in Elsevier Hélie, Thomas ELSEVIER Computable convergence bounds of series expansions for infinite dimensional linear-analytic systems and application 2014transfer abstract Amsterdam [u.a.] (DE-627)ELV017935458 volume:33 year:2020 number:6 pages:1703-1716 extent:14 https://doi.org/10.1016/j.cja.2020.02.022 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_63 GBV_ILN_70 44.48 Medizinische Genetik VZ AR 33 2020 6 1703-1716 14 |
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10.1016/j.cja.2020.02.022 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001036.pica (DE-627)ELV050630733 (ELSEVIER)S1000-9361(20)30087-X DE-627 ger DE-627 rakwb eng 000 VZ 620 VZ 610 VZ 44.48 bkl NIE, Wenming verfasserin aut Model-free adaptive optimal design for trajectory tracking control of rocket-powered vehicle 2020transfer abstract 14 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier An adaptive optimal trajectory tracking controller is presented for the Solid-Rocket-Powered Vehicle (SRPV) with uncertain nonlinear non-affine dynamics in the framework of adaptive dynamic programming. First, considering that the ascent model of the SRPV is non-affine, a model-free Single Network Adaptive Critic (SNAC) method is developed based on the dynamic neural network and the traditional SNAC method. This developed model-free SNAC method overcomes the limitation of the traditional SNAC method that can only be applied to affine systems. Then, a closed-form adaptive optimal controller is designed for the non-affine dynamics of SRPVs. This controller can adjust its parameters under different flight conditions and converge to the approximate optimal controller through online self-learning. Finally, the convergence to the approximate optimal controller is proved. The theoretical analysis of the uniformly ultimate boundedness of the tracking error is also presented. Simulation results demonstrate the effectiveness of the proposed controller. An adaptive optimal trajectory tracking controller is presented for the Solid-Rocket-Powered Vehicle (SRPV) with uncertain nonlinear non-affine dynamics in the framework of adaptive dynamic programming. First, considering that the ascent model of the SRPV is non-affine, a model-free Single Network Adaptive Critic (SNAC) method is developed based on the dynamic neural network and the traditional SNAC method. This developed model-free SNAC method overcomes the limitation of the traditional SNAC method that can only be applied to affine systems. Then, a closed-form adaptive optimal controller is designed for the non-affine dynamics of SRPVs. This controller can adjust its parameters under different flight conditions and converge to the approximate optimal controller through online self-learning. Finally, the convergence to the approximate optimal controller is proved. The theoretical analysis of the uniformly ultimate boundedness of the tracking error is also presented. Simulation results demonstrate the effectiveness of the proposed controller. Model-free Elsevier Trajectory tracking Elsevier Dynamic neural network Elsevier Adaptive dynamic programming Elsevier Solid-rocket-powered vehicle Elsevier LI, Huifeng oth ZHANG, Ran oth Enthalten in Elsevier Hélie, Thomas ELSEVIER Computable convergence bounds of series expansions for infinite dimensional linear-analytic systems and application 2014transfer abstract Amsterdam [u.a.] (DE-627)ELV017935458 volume:33 year:2020 number:6 pages:1703-1716 extent:14 https://doi.org/10.1016/j.cja.2020.02.022 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_63 GBV_ILN_70 44.48 Medizinische Genetik VZ AR 33 2020 6 1703-1716 14 |
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English |
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Enthalten in Computable convergence bounds of series expansions for infinite dimensional linear-analytic systems and application Amsterdam [u.a.] volume:33 year:2020 number:6 pages:1703-1716 extent:14 |
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Enthalten in Computable convergence bounds of series expansions for infinite dimensional linear-analytic systems and application Amsterdam [u.a.] volume:33 year:2020 number:6 pages:1703-1716 extent:14 |
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Computable convergence bounds of series expansions for infinite dimensional linear-analytic systems and application |
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Model-free adaptive optimal design for trajectory tracking control of rocket-powered vehicle |
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An adaptive optimal trajectory tracking controller is presented for the Solid-Rocket-Powered Vehicle (SRPV) with uncertain nonlinear non-affine dynamics in the framework of adaptive dynamic programming. First, considering that the ascent model of the SRPV is non-affine, a model-free Single Network Adaptive Critic (SNAC) method is developed based on the dynamic neural network and the traditional SNAC method. This developed model-free SNAC method overcomes the limitation of the traditional SNAC method that can only be applied to affine systems. Then, a closed-form adaptive optimal controller is designed for the non-affine dynamics of SRPVs. This controller can adjust its parameters under different flight conditions and converge to the approximate optimal controller through online self-learning. Finally, the convergence to the approximate optimal controller is proved. The theoretical analysis of the uniformly ultimate boundedness of the tracking error is also presented. Simulation results demonstrate the effectiveness of the proposed controller. |
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An adaptive optimal trajectory tracking controller is presented for the Solid-Rocket-Powered Vehicle (SRPV) with uncertain nonlinear non-affine dynamics in the framework of adaptive dynamic programming. First, considering that the ascent model of the SRPV is non-affine, a model-free Single Network Adaptive Critic (SNAC) method is developed based on the dynamic neural network and the traditional SNAC method. This developed model-free SNAC method overcomes the limitation of the traditional SNAC method that can only be applied to affine systems. Then, a closed-form adaptive optimal controller is designed for the non-affine dynamics of SRPVs. This controller can adjust its parameters under different flight conditions and converge to the approximate optimal controller through online self-learning. Finally, the convergence to the approximate optimal controller is proved. The theoretical analysis of the uniformly ultimate boundedness of the tracking error is also presented. Simulation results demonstrate the effectiveness of the proposed controller. |
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An adaptive optimal trajectory tracking controller is presented for the Solid-Rocket-Powered Vehicle (SRPV) with uncertain nonlinear non-affine dynamics in the framework of adaptive dynamic programming. First, considering that the ascent model of the SRPV is non-affine, a model-free Single Network Adaptive Critic (SNAC) method is developed based on the dynamic neural network and the traditional SNAC method. This developed model-free SNAC method overcomes the limitation of the traditional SNAC method that can only be applied to affine systems. Then, a closed-form adaptive optimal controller is designed for the non-affine dynamics of SRPVs. This controller can adjust its parameters under different flight conditions and converge to the approximate optimal controller through online self-learning. Finally, the convergence to the approximate optimal controller is proved. The theoretical analysis of the uniformly ultimate boundedness of the tracking error is also presented. Simulation results demonstrate the effectiveness of the proposed controller. |
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