Parameter identification using adaptive differential evolution algorithm applied to robust control of uncertain nonlinear systems
This paper investigates the dynamic parametric identification of the uncertain inverted pendulum system perturbed with friction by using an adaptive differential evolution (ADE) algorithm. In ADE approach, the initiation is realized in the mutant step with a new mutation scheme, namely adaptive muta...
Ausführliche Beschreibung
Autor*in: |
Anh, Ho Pham Huy [verfasserIn] Son, Nguyen Ngoc [verfasserIn] Van Kien, Cao [verfasserIn] Ho-Huu, V. [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2018 |
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Schlagwörter: |
Adaptive differential evolution (ADE) algorithm |
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Übergeordnetes Werk: |
Enthalten in: Applied soft computing - Amsterdam [u.a.] : Elsevier Science, 2001, 71, Seite 672-684 |
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Übergeordnetes Werk: |
volume:71 ; pages:672-684 |
DOI / URN: |
10.1016/j.asoc.2018.07.015 |
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Katalog-ID: |
ELV000538159 |
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520 | |a This paper investigates the dynamic parametric identification of the uncertain inverted pendulum system perturbed with friction by using an adaptive differential evolution (ADE) algorithm. In ADE approach, the initiation is realized in the mutant step with a new mutation scheme, namely adaptive mutant structure, contained multi-mutant vectors including ‘best/1’ and ‘best/2’ or ‘rand/1’ and ‘rand/2’ for selecting target vectors in population. The modification that aims to equalize between global exploration and local exploitation capacities which helps to effectively search global potential optimum solutions. The performance of ADE algorithm is compared with those of standard differential evolution (DE), particle swarm optimization (PSO) and genetic algorithm (GA). Furthermore, the identification results are applied to design a swing-up and balancing controller for the inverted pendulum system perturbed with friction. The sliding mode controller is used to swing up the inverted pendulum system to the top equilibrium position, and the LQR controller is initially applied for balancing and control the position of the first link of the inverted Pendulum in the downright position. The experimental results demonstrate that the proposed approach can accurately identify and robust control such nonlinear dynamic systems. | ||
650 | 4 | |a Adaptive differential evolution (ADE) algorithm | |
650 | 4 | |a Inverted pendulum system | |
650 | 4 | |a Dynamic parametric estimation | |
650 | 4 | |a Swing-up and balancing controller | |
650 | 4 | |a Parametric-model based LQR control | |
700 | 1 | |a Son, Nguyen Ngoc |e verfasserin |4 aut | |
700 | 1 | |a Van Kien, Cao |e verfasserin |4 aut | |
700 | 1 | |a Ho-Huu, V. |e verfasserin |4 aut | |
773 | 0 | 8 | |i Enthalten in |t Applied soft computing |d Amsterdam [u.a.] : Elsevier Science, 2001 |g 71, Seite 672-684 |h Online-Ressource |w (DE-627)334375754 |w (DE-600)2057709-6 |w (DE-576)256145733 |x 1568-4946 |7 nnns |
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10.1016/j.asoc.2018.07.015 doi (DE-627)ELV000538159 (ELSEVIER)S1568-4946(18)30403-4 DE-627 ger DE-627 rda eng 004 VZ 54.00 bkl Anh, Ho Pham Huy verfasserin aut Parameter identification using adaptive differential evolution algorithm applied to robust control of uncertain nonlinear systems 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper investigates the dynamic parametric identification of the uncertain inverted pendulum system perturbed with friction by using an adaptive differential evolution (ADE) algorithm. In ADE approach, the initiation is realized in the mutant step with a new mutation scheme, namely adaptive mutant structure, contained multi-mutant vectors including ‘best/1’ and ‘best/2’ or ‘rand/1’ and ‘rand/2’ for selecting target vectors in population. The modification that aims to equalize between global exploration and local exploitation capacities which helps to effectively search global potential optimum solutions. The performance of ADE algorithm is compared with those of standard differential evolution (DE), particle swarm optimization (PSO) and genetic algorithm (GA). Furthermore, the identification results are applied to design a swing-up and balancing controller for the inverted pendulum system perturbed with friction. The sliding mode controller is used to swing up the inverted pendulum system to the top equilibrium position, and the LQR controller is initially applied for balancing and control the position of the first link of the inverted Pendulum in the downright position. The experimental results demonstrate that the proposed approach can accurately identify and robust control such nonlinear dynamic systems. Adaptive differential evolution (ADE) algorithm Inverted pendulum system Dynamic parametric estimation Swing-up and balancing controller Parametric-model based LQR control Son, Nguyen Ngoc verfasserin aut Van Kien, Cao verfasserin aut Ho-Huu, V. verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 71, Seite 672-684 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:71 pages:672-684 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines VZ AR 71 672-684 |
spelling |
10.1016/j.asoc.2018.07.015 doi (DE-627)ELV000538159 (ELSEVIER)S1568-4946(18)30403-4 DE-627 ger DE-627 rda eng 004 VZ 54.00 bkl Anh, Ho Pham Huy verfasserin aut Parameter identification using adaptive differential evolution algorithm applied to robust control of uncertain nonlinear systems 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper investigates the dynamic parametric identification of the uncertain inverted pendulum system perturbed with friction by using an adaptive differential evolution (ADE) algorithm. In ADE approach, the initiation is realized in the mutant step with a new mutation scheme, namely adaptive mutant structure, contained multi-mutant vectors including ‘best/1’ and ‘best/2’ or ‘rand/1’ and ‘rand/2’ for selecting target vectors in population. The modification that aims to equalize between global exploration and local exploitation capacities which helps to effectively search global potential optimum solutions. The performance of ADE algorithm is compared with those of standard differential evolution (DE), particle swarm optimization (PSO) and genetic algorithm (GA). Furthermore, the identification results are applied to design a swing-up and balancing controller for the inverted pendulum system perturbed with friction. The sliding mode controller is used to swing up the inverted pendulum system to the top equilibrium position, and the LQR controller is initially applied for balancing and control the position of the first link of the inverted Pendulum in the downright position. The experimental results demonstrate that the proposed approach can accurately identify and robust control such nonlinear dynamic systems. Adaptive differential evolution (ADE) algorithm Inverted pendulum system Dynamic parametric estimation Swing-up and balancing controller Parametric-model based LQR control Son, Nguyen Ngoc verfasserin aut Van Kien, Cao verfasserin aut Ho-Huu, V. verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 71, Seite 672-684 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:71 pages:672-684 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines VZ AR 71 672-684 |
allfields_unstemmed |
10.1016/j.asoc.2018.07.015 doi (DE-627)ELV000538159 (ELSEVIER)S1568-4946(18)30403-4 DE-627 ger DE-627 rda eng 004 VZ 54.00 bkl Anh, Ho Pham Huy verfasserin aut Parameter identification using adaptive differential evolution algorithm applied to robust control of uncertain nonlinear systems 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper investigates the dynamic parametric identification of the uncertain inverted pendulum system perturbed with friction by using an adaptive differential evolution (ADE) algorithm. In ADE approach, the initiation is realized in the mutant step with a new mutation scheme, namely adaptive mutant structure, contained multi-mutant vectors including ‘best/1’ and ‘best/2’ or ‘rand/1’ and ‘rand/2’ for selecting target vectors in population. The modification that aims to equalize between global exploration and local exploitation capacities which helps to effectively search global potential optimum solutions. The performance of ADE algorithm is compared with those of standard differential evolution (DE), particle swarm optimization (PSO) and genetic algorithm (GA). Furthermore, the identification results are applied to design a swing-up and balancing controller for the inverted pendulum system perturbed with friction. The sliding mode controller is used to swing up the inverted pendulum system to the top equilibrium position, and the LQR controller is initially applied for balancing and control the position of the first link of the inverted Pendulum in the downright position. The experimental results demonstrate that the proposed approach can accurately identify and robust control such nonlinear dynamic systems. Adaptive differential evolution (ADE) algorithm Inverted pendulum system Dynamic parametric estimation Swing-up and balancing controller Parametric-model based LQR control Son, Nguyen Ngoc verfasserin aut Van Kien, Cao verfasserin aut Ho-Huu, V. verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 71, Seite 672-684 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:71 pages:672-684 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines VZ AR 71 672-684 |
allfieldsGer |
10.1016/j.asoc.2018.07.015 doi (DE-627)ELV000538159 (ELSEVIER)S1568-4946(18)30403-4 DE-627 ger DE-627 rda eng 004 VZ 54.00 bkl Anh, Ho Pham Huy verfasserin aut Parameter identification using adaptive differential evolution algorithm applied to robust control of uncertain nonlinear systems 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper investigates the dynamic parametric identification of the uncertain inverted pendulum system perturbed with friction by using an adaptive differential evolution (ADE) algorithm. In ADE approach, the initiation is realized in the mutant step with a new mutation scheme, namely adaptive mutant structure, contained multi-mutant vectors including ‘best/1’ and ‘best/2’ or ‘rand/1’ and ‘rand/2’ for selecting target vectors in population. The modification that aims to equalize between global exploration and local exploitation capacities which helps to effectively search global potential optimum solutions. The performance of ADE algorithm is compared with those of standard differential evolution (DE), particle swarm optimization (PSO) and genetic algorithm (GA). Furthermore, the identification results are applied to design a swing-up and balancing controller for the inverted pendulum system perturbed with friction. The sliding mode controller is used to swing up the inverted pendulum system to the top equilibrium position, and the LQR controller is initially applied for balancing and control the position of the first link of the inverted Pendulum in the downright position. The experimental results demonstrate that the proposed approach can accurately identify and robust control such nonlinear dynamic systems. Adaptive differential evolution (ADE) algorithm Inverted pendulum system Dynamic parametric estimation Swing-up and balancing controller Parametric-model based LQR control Son, Nguyen Ngoc verfasserin aut Van Kien, Cao verfasserin aut Ho-Huu, V. verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 71, Seite 672-684 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:71 pages:672-684 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines VZ AR 71 672-684 |
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10.1016/j.asoc.2018.07.015 doi (DE-627)ELV000538159 (ELSEVIER)S1568-4946(18)30403-4 DE-627 ger DE-627 rda eng 004 VZ 54.00 bkl Anh, Ho Pham Huy verfasserin aut Parameter identification using adaptive differential evolution algorithm applied to robust control of uncertain nonlinear systems 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper investigates the dynamic parametric identification of the uncertain inverted pendulum system perturbed with friction by using an adaptive differential evolution (ADE) algorithm. In ADE approach, the initiation is realized in the mutant step with a new mutation scheme, namely adaptive mutant structure, contained multi-mutant vectors including ‘best/1’ and ‘best/2’ or ‘rand/1’ and ‘rand/2’ for selecting target vectors in population. The modification that aims to equalize between global exploration and local exploitation capacities which helps to effectively search global potential optimum solutions. The performance of ADE algorithm is compared with those of standard differential evolution (DE), particle swarm optimization (PSO) and genetic algorithm (GA). Furthermore, the identification results are applied to design a swing-up and balancing controller for the inverted pendulum system perturbed with friction. The sliding mode controller is used to swing up the inverted pendulum system to the top equilibrium position, and the LQR controller is initially applied for balancing and control the position of the first link of the inverted Pendulum in the downright position. The experimental results demonstrate that the proposed approach can accurately identify and robust control such nonlinear dynamic systems. Adaptive differential evolution (ADE) algorithm Inverted pendulum system Dynamic parametric estimation Swing-up and balancing controller Parametric-model based LQR control Son, Nguyen Ngoc verfasserin aut Van Kien, Cao verfasserin aut Ho-Huu, V. verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 71, Seite 672-684 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:71 pages:672-684 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines VZ AR 71 672-684 |
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ddc 004 bkl 54.00 misc Adaptive differential evolution (ADE) algorithm misc Inverted pendulum system misc Dynamic parametric estimation misc Swing-up and balancing controller misc Parametric-model based LQR control |
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ddc 004 bkl 54.00 misc Adaptive differential evolution (ADE) algorithm misc Inverted pendulum system misc Dynamic parametric estimation misc Swing-up and balancing controller misc Parametric-model based LQR control |
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Parameter identification using adaptive differential evolution algorithm applied to robust control of uncertain nonlinear systems |
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Parameter identification using adaptive differential evolution algorithm applied to robust control of uncertain nonlinear systems |
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Anh, Ho Pham Huy |
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Applied soft computing |
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Anh, Ho Pham Huy Son, Nguyen Ngoc Van Kien, Cao Ho-Huu, V. |
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Anh, Ho Pham Huy |
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10.1016/j.asoc.2018.07.015 |
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parameter identification using adaptive differential evolution algorithm applied to robust control of uncertain nonlinear systems |
title_auth |
Parameter identification using adaptive differential evolution algorithm applied to robust control of uncertain nonlinear systems |
abstract |
This paper investigates the dynamic parametric identification of the uncertain inverted pendulum system perturbed with friction by using an adaptive differential evolution (ADE) algorithm. In ADE approach, the initiation is realized in the mutant step with a new mutation scheme, namely adaptive mutant structure, contained multi-mutant vectors including ‘best/1’ and ‘best/2’ or ‘rand/1’ and ‘rand/2’ for selecting target vectors in population. The modification that aims to equalize between global exploration and local exploitation capacities which helps to effectively search global potential optimum solutions. The performance of ADE algorithm is compared with those of standard differential evolution (DE), particle swarm optimization (PSO) and genetic algorithm (GA). Furthermore, the identification results are applied to design a swing-up and balancing controller for the inverted pendulum system perturbed with friction. The sliding mode controller is used to swing up the inverted pendulum system to the top equilibrium position, and the LQR controller is initially applied for balancing and control the position of the first link of the inverted Pendulum in the downright position. The experimental results demonstrate that the proposed approach can accurately identify and robust control such nonlinear dynamic systems. |
abstractGer |
This paper investigates the dynamic parametric identification of the uncertain inverted pendulum system perturbed with friction by using an adaptive differential evolution (ADE) algorithm. In ADE approach, the initiation is realized in the mutant step with a new mutation scheme, namely adaptive mutant structure, contained multi-mutant vectors including ‘best/1’ and ‘best/2’ or ‘rand/1’ and ‘rand/2’ for selecting target vectors in population. The modification that aims to equalize between global exploration and local exploitation capacities which helps to effectively search global potential optimum solutions. The performance of ADE algorithm is compared with those of standard differential evolution (DE), particle swarm optimization (PSO) and genetic algorithm (GA). Furthermore, the identification results are applied to design a swing-up and balancing controller for the inverted pendulum system perturbed with friction. The sliding mode controller is used to swing up the inverted pendulum system to the top equilibrium position, and the LQR controller is initially applied for balancing and control the position of the first link of the inverted Pendulum in the downright position. The experimental results demonstrate that the proposed approach can accurately identify and robust control such nonlinear dynamic systems. |
abstract_unstemmed |
This paper investigates the dynamic parametric identification of the uncertain inverted pendulum system perturbed with friction by using an adaptive differential evolution (ADE) algorithm. In ADE approach, the initiation is realized in the mutant step with a new mutation scheme, namely adaptive mutant structure, contained multi-mutant vectors including ‘best/1’ and ‘best/2’ or ‘rand/1’ and ‘rand/2’ for selecting target vectors in population. The modification that aims to equalize between global exploration and local exploitation capacities which helps to effectively search global potential optimum solutions. The performance of ADE algorithm is compared with those of standard differential evolution (DE), particle swarm optimization (PSO) and genetic algorithm (GA). Furthermore, the identification results are applied to design a swing-up and balancing controller for the inverted pendulum system perturbed with friction. The sliding mode controller is used to swing up the inverted pendulum system to the top equilibrium position, and the LQR controller is initially applied for balancing and control the position of the first link of the inverted Pendulum in the downright position. The experimental results demonstrate that the proposed approach can accurately identify and robust control such nonlinear dynamic systems. |
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title_short |
Parameter identification using adaptive differential evolution algorithm applied to robust control of uncertain nonlinear systems |
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Son, Nguyen Ngoc Van Kien, Cao Ho-Huu, V. |
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up_date |
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