Adaptive inverse multilayer fuzzy control for uncertain nonlinear system optimizing with differential evolution algorithm
Abstract This paper introduces a novel adaptive inverse multilayer T-S fuzzy controller (AIMFC) optimally identified with an optimization soft computing algorithm available for a class of robust control applied in uncertain nonlinear SISO systems. The parameters of multilayer T-S fuzzy model are opt...
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| Autor*in: |
Van Kien, Cao [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
Adaptive inverse multilayer T-S fuzzy controller (AIMFC) Hybrid adaptive optimal control Differential evolution (DE) algorithm |
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| Anmerkung: |
© Springer Science+Business Media, LLC, part of Springer Nature 2020 |
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| Übergeordnetes Werk: |
Enthalten in: Applied intelligence - Springer US, 1991, 51(2020), 1 vom: 20. Aug., Seite 527-548 |
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| Übergeordnetes Werk: |
volume:51 ; year:2020 ; number:1 ; day:20 ; month:08 ; pages:527-548 |
| Links: |
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| DOI / URN: |
10.1007/s10489-020-01819-9 |
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| Katalog-ID: |
OLC2122314966 |
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| 520 | |a Abstract This paper introduces a novel adaptive inverse multilayer T-S fuzzy controller (AIMFC) optimally identified with an optimization soft computing algorithm available for a class of robust control applied in uncertain nonlinear SISO systems. The parameters of multilayer T-S fuzzy model are optimally identified by the differential evolution (DE) algorithm to create offline the inverse nonlinear plant with uncertain coefficients. Then, the adaptive fuzzy-based sliding mode surface is applied to ensure that the closed-loop system is asymptotically stable in which the stability is satisfied using Lyapunov stability concept. The control quality of the proposed AIMFC algorithm is compared with the three recent advanced control algorithms applied in the Spring-Mass-Damper (SMD) benchmark system. Simulation and experiment results with different control parameters show that the proposed algorithm is better than the inverse fuzzy controller and the conventional adaptive fuzzy controller comparatively applied in both SMD system and the coupled-liquid tank system with the performance index using the least mean squares (LMS) error, which is investigated to demonstrate the efficiency and the robustness of the proposed AIMFC control approach. | ||
| 650 | 4 | |a Adaptive inverse multilayer T-S fuzzy controller (AIMFC) | |
| 650 | 4 | |a Uncertain nonlinear system | |
| 650 | 4 | |a Hybrid adaptive optimal control | |
| 650 | 4 | |a Differential evolution (DE) algorithm | |
| 650 | 4 | |a Lyapunov stability principle | |
| 650 | 4 | |a Spring-mass-damper (SMD) benchmark system | |
| 650 | 4 | |a Coupled-liquid tank system | |
| 700 | 1 | |a Anh, Ho Pham Huy |0 (orcid)0000-0001-7353-8205 |4 aut | |
| 700 | 1 | |a Son, Nguyen Ngoc |4 aut | |
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10.1007/s10489-020-01819-9 doi (DE-627)OLC2122314966 (DE-He213)s10489-020-01819-9-p DE-627 ger DE-627 rakwb eng 004 VZ Van Kien, Cao verfasserin aut Adaptive inverse multilayer fuzzy control for uncertain nonlinear system optimizing with differential evolution algorithm 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract This paper introduces a novel adaptive inverse multilayer T-S fuzzy controller (AIMFC) optimally identified with an optimization soft computing algorithm available for a class of robust control applied in uncertain nonlinear SISO systems. The parameters of multilayer T-S fuzzy model are optimally identified by the differential evolution (DE) algorithm to create offline the inverse nonlinear plant with uncertain coefficients. Then, the adaptive fuzzy-based sliding mode surface is applied to ensure that the closed-loop system is asymptotically stable in which the stability is satisfied using Lyapunov stability concept. The control quality of the proposed AIMFC algorithm is compared with the three recent advanced control algorithms applied in the Spring-Mass-Damper (SMD) benchmark system. Simulation and experiment results with different control parameters show that the proposed algorithm is better than the inverse fuzzy controller and the conventional adaptive fuzzy controller comparatively applied in both SMD system and the coupled-liquid tank system with the performance index using the least mean squares (LMS) error, which is investigated to demonstrate the efficiency and the robustness of the proposed AIMFC control approach. Adaptive inverse multilayer T-S fuzzy controller (AIMFC) Uncertain nonlinear system Hybrid adaptive optimal control Differential evolution (DE) algorithm Lyapunov stability principle Spring-mass-damper (SMD) benchmark system Coupled-liquid tank system Anh, Ho Pham Huy (orcid)0000-0001-7353-8205 aut Son, Nguyen Ngoc aut Enthalten in Applied intelligence Springer US, 1991 51(2020), 1 vom: 20. Aug., Seite 527-548 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:51 year:2020 number:1 day:20 month:08 pages:527-548 https://doi.org/10.1007/s10489-020-01819-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 51 2020 1 20 08 527-548 |
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10.1007/s10489-020-01819-9 doi (DE-627)OLC2122314966 (DE-He213)s10489-020-01819-9-p DE-627 ger DE-627 rakwb eng 004 VZ Van Kien, Cao verfasserin aut Adaptive inverse multilayer fuzzy control for uncertain nonlinear system optimizing with differential evolution algorithm 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract This paper introduces a novel adaptive inverse multilayer T-S fuzzy controller (AIMFC) optimally identified with an optimization soft computing algorithm available for a class of robust control applied in uncertain nonlinear SISO systems. The parameters of multilayer T-S fuzzy model are optimally identified by the differential evolution (DE) algorithm to create offline the inverse nonlinear plant with uncertain coefficients. Then, the adaptive fuzzy-based sliding mode surface is applied to ensure that the closed-loop system is asymptotically stable in which the stability is satisfied using Lyapunov stability concept. The control quality of the proposed AIMFC algorithm is compared with the three recent advanced control algorithms applied in the Spring-Mass-Damper (SMD) benchmark system. Simulation and experiment results with different control parameters show that the proposed algorithm is better than the inverse fuzzy controller and the conventional adaptive fuzzy controller comparatively applied in both SMD system and the coupled-liquid tank system with the performance index using the least mean squares (LMS) error, which is investigated to demonstrate the efficiency and the robustness of the proposed AIMFC control approach. Adaptive inverse multilayer T-S fuzzy controller (AIMFC) Uncertain nonlinear system Hybrid adaptive optimal control Differential evolution (DE) algorithm Lyapunov stability principle Spring-mass-damper (SMD) benchmark system Coupled-liquid tank system Anh, Ho Pham Huy (orcid)0000-0001-7353-8205 aut Son, Nguyen Ngoc aut Enthalten in Applied intelligence Springer US, 1991 51(2020), 1 vom: 20. Aug., Seite 527-548 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:51 year:2020 number:1 day:20 month:08 pages:527-548 https://doi.org/10.1007/s10489-020-01819-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 51 2020 1 20 08 527-548 |
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10.1007/s10489-020-01819-9 doi (DE-627)OLC2122314966 (DE-He213)s10489-020-01819-9-p DE-627 ger DE-627 rakwb eng 004 VZ Van Kien, Cao verfasserin aut Adaptive inverse multilayer fuzzy control for uncertain nonlinear system optimizing with differential evolution algorithm 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract This paper introduces a novel adaptive inverse multilayer T-S fuzzy controller (AIMFC) optimally identified with an optimization soft computing algorithm available for a class of robust control applied in uncertain nonlinear SISO systems. The parameters of multilayer T-S fuzzy model are optimally identified by the differential evolution (DE) algorithm to create offline the inverse nonlinear plant with uncertain coefficients. Then, the adaptive fuzzy-based sliding mode surface is applied to ensure that the closed-loop system is asymptotically stable in which the stability is satisfied using Lyapunov stability concept. The control quality of the proposed AIMFC algorithm is compared with the three recent advanced control algorithms applied in the Spring-Mass-Damper (SMD) benchmark system. Simulation and experiment results with different control parameters show that the proposed algorithm is better than the inverse fuzzy controller and the conventional adaptive fuzzy controller comparatively applied in both SMD system and the coupled-liquid tank system with the performance index using the least mean squares (LMS) error, which is investigated to demonstrate the efficiency and the robustness of the proposed AIMFC control approach. Adaptive inverse multilayer T-S fuzzy controller (AIMFC) Uncertain nonlinear system Hybrid adaptive optimal control Differential evolution (DE) algorithm Lyapunov stability principle Spring-mass-damper (SMD) benchmark system Coupled-liquid tank system Anh, Ho Pham Huy (orcid)0000-0001-7353-8205 aut Son, Nguyen Ngoc aut Enthalten in Applied intelligence Springer US, 1991 51(2020), 1 vom: 20. Aug., Seite 527-548 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:51 year:2020 number:1 day:20 month:08 pages:527-548 https://doi.org/10.1007/s10489-020-01819-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 51 2020 1 20 08 527-548 |
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10.1007/s10489-020-01819-9 doi (DE-627)OLC2122314966 (DE-He213)s10489-020-01819-9-p DE-627 ger DE-627 rakwb eng 004 VZ Van Kien, Cao verfasserin aut Adaptive inverse multilayer fuzzy control for uncertain nonlinear system optimizing with differential evolution algorithm 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract This paper introduces a novel adaptive inverse multilayer T-S fuzzy controller (AIMFC) optimally identified with an optimization soft computing algorithm available for a class of robust control applied in uncertain nonlinear SISO systems. The parameters of multilayer T-S fuzzy model are optimally identified by the differential evolution (DE) algorithm to create offline the inverse nonlinear plant with uncertain coefficients. Then, the adaptive fuzzy-based sliding mode surface is applied to ensure that the closed-loop system is asymptotically stable in which the stability is satisfied using Lyapunov stability concept. The control quality of the proposed AIMFC algorithm is compared with the three recent advanced control algorithms applied in the Spring-Mass-Damper (SMD) benchmark system. Simulation and experiment results with different control parameters show that the proposed algorithm is better than the inverse fuzzy controller and the conventional adaptive fuzzy controller comparatively applied in both SMD system and the coupled-liquid tank system with the performance index using the least mean squares (LMS) error, which is investigated to demonstrate the efficiency and the robustness of the proposed AIMFC control approach. Adaptive inverse multilayer T-S fuzzy controller (AIMFC) Uncertain nonlinear system Hybrid adaptive optimal control Differential evolution (DE) algorithm Lyapunov stability principle Spring-mass-damper (SMD) benchmark system Coupled-liquid tank system Anh, Ho Pham Huy (orcid)0000-0001-7353-8205 aut Son, Nguyen Ngoc aut Enthalten in Applied intelligence Springer US, 1991 51(2020), 1 vom: 20. Aug., Seite 527-548 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:51 year:2020 number:1 day:20 month:08 pages:527-548 https://doi.org/10.1007/s10489-020-01819-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 51 2020 1 20 08 527-548 |
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10.1007/s10489-020-01819-9 doi (DE-627)OLC2122314966 (DE-He213)s10489-020-01819-9-p DE-627 ger DE-627 rakwb eng 004 VZ Van Kien, Cao verfasserin aut Adaptive inverse multilayer fuzzy control for uncertain nonlinear system optimizing with differential evolution algorithm 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract This paper introduces a novel adaptive inverse multilayer T-S fuzzy controller (AIMFC) optimally identified with an optimization soft computing algorithm available for a class of robust control applied in uncertain nonlinear SISO systems. The parameters of multilayer T-S fuzzy model are optimally identified by the differential evolution (DE) algorithm to create offline the inverse nonlinear plant with uncertain coefficients. Then, the adaptive fuzzy-based sliding mode surface is applied to ensure that the closed-loop system is asymptotically stable in which the stability is satisfied using Lyapunov stability concept. The control quality of the proposed AIMFC algorithm is compared with the three recent advanced control algorithms applied in the Spring-Mass-Damper (SMD) benchmark system. Simulation and experiment results with different control parameters show that the proposed algorithm is better than the inverse fuzzy controller and the conventional adaptive fuzzy controller comparatively applied in both SMD system and the coupled-liquid tank system with the performance index using the least mean squares (LMS) error, which is investigated to demonstrate the efficiency and the robustness of the proposed AIMFC control approach. Adaptive inverse multilayer T-S fuzzy controller (AIMFC) Uncertain nonlinear system Hybrid adaptive optimal control Differential evolution (DE) algorithm Lyapunov stability principle Spring-mass-damper (SMD) benchmark system Coupled-liquid tank system Anh, Ho Pham Huy (orcid)0000-0001-7353-8205 aut Son, Nguyen Ngoc aut Enthalten in Applied intelligence Springer US, 1991 51(2020), 1 vom: 20. Aug., Seite 527-548 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:51 year:2020 number:1 day:20 month:08 pages:527-548 https://doi.org/10.1007/s10489-020-01819-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 51 2020 1 20 08 527-548 |
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Adaptive inverse multilayer fuzzy control for uncertain nonlinear system optimizing with differential evolution algorithm |
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Abstract This paper introduces a novel adaptive inverse multilayer T-S fuzzy controller (AIMFC) optimally identified with an optimization soft computing algorithm available for a class of robust control applied in uncertain nonlinear SISO systems. The parameters of multilayer T-S fuzzy model are optimally identified by the differential evolution (DE) algorithm to create offline the inverse nonlinear plant with uncertain coefficients. Then, the adaptive fuzzy-based sliding mode surface is applied to ensure that the closed-loop system is asymptotically stable in which the stability is satisfied using Lyapunov stability concept. The control quality of the proposed AIMFC algorithm is compared with the three recent advanced control algorithms applied in the Spring-Mass-Damper (SMD) benchmark system. Simulation and experiment results with different control parameters show that the proposed algorithm is better than the inverse fuzzy controller and the conventional adaptive fuzzy controller comparatively applied in both SMD system and the coupled-liquid tank system with the performance index using the least mean squares (LMS) error, which is investigated to demonstrate the efficiency and the robustness of the proposed AIMFC control approach. © Springer Science+Business Media, LLC, part of Springer Nature 2020 |
| abstractGer |
Abstract This paper introduces a novel adaptive inverse multilayer T-S fuzzy controller (AIMFC) optimally identified with an optimization soft computing algorithm available for a class of robust control applied in uncertain nonlinear SISO systems. The parameters of multilayer T-S fuzzy model are optimally identified by the differential evolution (DE) algorithm to create offline the inverse nonlinear plant with uncertain coefficients. Then, the adaptive fuzzy-based sliding mode surface is applied to ensure that the closed-loop system is asymptotically stable in which the stability is satisfied using Lyapunov stability concept. The control quality of the proposed AIMFC algorithm is compared with the three recent advanced control algorithms applied in the Spring-Mass-Damper (SMD) benchmark system. Simulation and experiment results with different control parameters show that the proposed algorithm is better than the inverse fuzzy controller and the conventional adaptive fuzzy controller comparatively applied in both SMD system and the coupled-liquid tank system with the performance index using the least mean squares (LMS) error, which is investigated to demonstrate the efficiency and the robustness of the proposed AIMFC control approach. © Springer Science+Business Media, LLC, part of Springer Nature 2020 |
| abstract_unstemmed |
Abstract This paper introduces a novel adaptive inverse multilayer T-S fuzzy controller (AIMFC) optimally identified with an optimization soft computing algorithm available for a class of robust control applied in uncertain nonlinear SISO systems. The parameters of multilayer T-S fuzzy model are optimally identified by the differential evolution (DE) algorithm to create offline the inverse nonlinear plant with uncertain coefficients. Then, the adaptive fuzzy-based sliding mode surface is applied to ensure that the closed-loop system is asymptotically stable in which the stability is satisfied using Lyapunov stability concept. The control quality of the proposed AIMFC algorithm is compared with the three recent advanced control algorithms applied in the Spring-Mass-Damper (SMD) benchmark system. Simulation and experiment results with different control parameters show that the proposed algorithm is better than the inverse fuzzy controller and the conventional adaptive fuzzy controller comparatively applied in both SMD system and the coupled-liquid tank system with the performance index using the least mean squares (LMS) error, which is investigated to demonstrate the efficiency and the robustness of the proposed AIMFC control approach. © Springer Science+Business Media, LLC, part of Springer Nature 2020 |
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Adaptive inverse multilayer fuzzy control for uncertain nonlinear system optimizing with differential evolution algorithm |
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https://doi.org/10.1007/s10489-020-01819-9 |
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| author2 |
Anh, Ho Pham Huy Son, Nguyen Ngoc |
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Anh, Ho Pham Huy Son, Nguyen Ngoc |
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| doi_str |
10.1007/s10489-020-01819-9 |
| up_date |
2024-07-04T09:45:23.343Z |
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