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...
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Autor*in:	Anh, Ho Pham Huy [verfasserIn] Son, Nguyen Ngoc [verfasserIn] Van Kien, Cao [verfasserIn] Ho-Huu, V. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Adaptive differential evolution (ADE) algorithm Inverted pendulum system Dynamic parametric estimation Swing-up and balancing controller Parametric-model based LQR control

Übergeordnetes Werk:	Enthalten in: Applied soft computing - Amsterdam [u.a.] : Elsevier Science, 2001, 71, Seite 672-684
Übergeordnetes Werk:	volume:71 ; pages:672-684

DOI / URN:	10.1016/j.asoc.2018.07.015

Katalog-ID:	ELV000538159

Internformat


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245	1	0	\|a Parameter identification using adaptive differential evolution algorithm applied to robust control of uncertain nonlinear systems
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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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912			\|a GBV_ILN_73
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912			\|a GBV_ILN_90
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_101
912			\|a GBV_ILN_105
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912			\|a GBV_ILN_150
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912			\|a GBV_ILN_224
912			\|a GBV_ILN_370
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912			\|a GBV_ILN_2034
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912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2065
912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2088
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912			\|a GBV_ILN_2112
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912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
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912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
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912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4393
936	b	k	\|a 54.00 \|j Informatik: Allgemeines \|q VZ
951			\|a AR
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Indexfelder

author_variant	h p h a hph hpha n n s nn nns k c v kc kcv v h h vhh
matchkey_str	article:15684946:2018----::aaeeietfctouigdpieifrnilvltoagrtmpletrbscn
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publishDate	2018
allfields	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
allfieldsSound	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
language	English
source	Enthalten in Applied soft computing 71, Seite 672-684 volume:71 pages:672-684
sourceStr	Enthalten in Applied soft computing 71, Seite 672-684 volume:71 pages:672-684
format_phy_str_mv	Article
bklname	Informatik: Allgemeines
institution	findex.gbv.de
topic_facet	Adaptive differential evolution (ADE) algorithm Inverted pendulum system Dynamic parametric estimation Swing-up and balancing controller Parametric-model based LQR control
dewey-raw	004
isfreeaccess_bool	false
container_title	Applied soft computing
authorswithroles_txt_mv	Anh, Ho Pham Huy @@aut@@ Son, Nguyen Ngoc @@aut@@ Van Kien, Cao @@aut@@ Ho-Huu, V. @@aut@@
publishDateDaySort_date	2018-01-01T00:00:00Z
hierarchy_top_id	334375754
dewey-sort	14
id	ELV000538159
language_de	englisch
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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. 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author	Anh, Ho Pham Huy
spellingShingle	Anh, Ho Pham Huy 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 Parameter identification using adaptive differential evolution algorithm applied to robust control of uncertain nonlinear systems
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topic_title	004 VZ 54.00 bkl Parameter identification using adaptive differential evolution algorithm applied to robust control of uncertain nonlinear systems Adaptive differential evolution (ADE) algorithm Inverted pendulum system Dynamic parametric estimation Swing-up and balancing controller Parametric-model based LQR control
topic	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
topic_unstemmed	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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title	Parameter identification using adaptive differential evolution algorithm applied to robust control of uncertain nonlinear systems
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title_full	Parameter identification using adaptive differential evolution algorithm applied to robust control of uncertain nonlinear systems
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title_sort	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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author2	Son, Nguyen Ngoc Van Kien, Cao Ho-Huu, V.
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up_date	2024-07-06T18:18:48.130Z
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Parameter identification using adaptive differential evolution algorithm applied to robust control of uncertain nonlinear systems

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