Air–fuel ratio prediction and NMPC for SI engines with modified Volterra model and RBF network
The dynamics of air manifold and fuel injection in spark ignition (SI) engines are modelled with both a modified Volterra series and a radial basis function (RBF) network. In a Volterra model-based model predictive control (MPC) the global optimal control can always be solved by a linear optimizatio...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Shi, Yiran [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015transfer abstract |
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| Schlagwörter: |
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| Umfang: |
12 |
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| Übergeordnetes Werk: |
Enthalten in: Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation - Liu, Xiang ELSEVIER, 2015, the international journal of real-time automation : a journal affiliated with IFAC, the International Federation of Automatic Control, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:45 ; year:2015 ; pages:313-324 ; extent:12 |
| Links: |
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| DOI / URN: |
10.1016/j.engappai.2015.07.008 |
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ELV039834603 |
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| 520 | |a The dynamics of air manifold and fuel injection in spark ignition (SI) engines are modelled with both a modified Volterra series and a radial basis function (RBF) network. In a Volterra model-based model predictive control (MPC) the global optimal control can always be solved by a linear optimization, but the model accuracy is low. On contrast, the RBF model provides more accurate prediction but its nonlinearity makes optimization nonlinear and non-convex, and therefore not always solvable. In this paper, the two models are combined so that linear optimization is enabled and the prediction accuracy is compensated, and therefore, both reliability and accuracy are achieved in a relative low computing cost. Using the developed method, the nonlinear MPC (NMPC) of the air/fuel ratio is implemented and evaluated by computer simulation. A real-time simulation using d-SPACE is also conducted to assess the real-time execution of the software. The simulation results show that the RBF compensated MPC outperformed over the Volterra model or RBF model based control. | ||
| 520 | |a The dynamics of air manifold and fuel injection in spark ignition (SI) engines are modelled with both a modified Volterra series and a radial basis function (RBF) network. In a Volterra model-based model predictive control (MPC) the global optimal control can always be solved by a linear optimization, but the model accuracy is low. On contrast, the RBF model provides more accurate prediction but its nonlinearity makes optimization nonlinear and non-convex, and therefore not always solvable. In this paper, the two models are combined so that linear optimization is enabled and the prediction accuracy is compensated, and therefore, both reliability and accuracy are achieved in a relative low computing cost. Using the developed method, the nonlinear MPC (NMPC) of the air/fuel ratio is implemented and evaluated by computer simulation. A real-time simulation using d-SPACE is also conducted to assess the real-time execution of the software. The simulation results show that the RBF compensated MPC outperformed over the Volterra model or RBF model based control. | ||
| 650 | 7 | |a RBF model |2 Elsevier | |
| 650 | 7 | |a Nonlinear model predictive control |2 Elsevier | |
| 650 | 7 | |a Air/fuel ratio control |2 Elsevier | |
| 650 | 7 | |a SI engines |2 Elsevier | |
| 650 | 7 | |a Volterra model |2 Elsevier | |
| 700 | 1 | |a Yu, Ding-Li |4 oth | |
| 700 | 1 | |a Tian, Yantao |4 oth | |
| 700 | 1 | |a Shi, Yaowu |4 oth | |
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10.1016/j.engappai.2015.07.008 doi GBVA2015016000002.pica (DE-627)ELV039834603 (ELSEVIER)S0952-1976(15)00155-4 DE-627 ger DE-627 rakwb eng 004 004 DE-600 540 VZ 610 VZ 44.00 bkl Shi, Yiran verfasserin aut Air–fuel ratio prediction and NMPC for SI engines with modified Volterra model and RBF network 2015transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The dynamics of air manifold and fuel injection in spark ignition (SI) engines are modelled with both a modified Volterra series and a radial basis function (RBF) network. In a Volterra model-based model predictive control (MPC) the global optimal control can always be solved by a linear optimization, but the model accuracy is low. On contrast, the RBF model provides more accurate prediction but its nonlinearity makes optimization nonlinear and non-convex, and therefore not always solvable. In this paper, the two models are combined so that linear optimization is enabled and the prediction accuracy is compensated, and therefore, both reliability and accuracy are achieved in a relative low computing cost. Using the developed method, the nonlinear MPC (NMPC) of the air/fuel ratio is implemented and evaluated by computer simulation. A real-time simulation using d-SPACE is also conducted to assess the real-time execution of the software. The simulation results show that the RBF compensated MPC outperformed over the Volterra model or RBF model based control. The dynamics of air manifold and fuel injection in spark ignition (SI) engines are modelled with both a modified Volterra series and a radial basis function (RBF) network. In a Volterra model-based model predictive control (MPC) the global optimal control can always be solved by a linear optimization, but the model accuracy is low. On contrast, the RBF model provides more accurate prediction but its nonlinearity makes optimization nonlinear and non-convex, and therefore not always solvable. In this paper, the two models are combined so that linear optimization is enabled and the prediction accuracy is compensated, and therefore, both reliability and accuracy are achieved in a relative low computing cost. Using the developed method, the nonlinear MPC (NMPC) of the air/fuel ratio is implemented and evaluated by computer simulation. A real-time simulation using d-SPACE is also conducted to assess the real-time execution of the software. The simulation results show that the RBF compensated MPC outperformed over the Volterra model or RBF model based control. RBF model Elsevier Nonlinear model predictive control Elsevier Air/fuel ratio control Elsevier SI engines Elsevier Volterra model Elsevier Yu, Ding-Li oth Tian, Yantao oth Shi, Yaowu oth Enthalten in Elsevier Science Liu, Xiang ELSEVIER Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation 2015 the international journal of real-time automation : a journal affiliated with IFAC, the International Federation of Automatic Control Amsterdam [u.a.] (DE-627)ELV013402978 volume:45 year:2015 pages:313-324 extent:12 https://doi.org/10.1016/j.engappai.2015.07.008 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.00 Medizin: Allgemeines VZ AR 45 2015 313-324 12 045F 004 |
| spelling |
10.1016/j.engappai.2015.07.008 doi GBVA2015016000002.pica (DE-627)ELV039834603 (ELSEVIER)S0952-1976(15)00155-4 DE-627 ger DE-627 rakwb eng 004 004 DE-600 540 VZ 610 VZ 44.00 bkl Shi, Yiran verfasserin aut Air–fuel ratio prediction and NMPC for SI engines with modified Volterra model and RBF network 2015transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The dynamics of air manifold and fuel injection in spark ignition (SI) engines are modelled with both a modified Volterra series and a radial basis function (RBF) network. In a Volterra model-based model predictive control (MPC) the global optimal control can always be solved by a linear optimization, but the model accuracy is low. On contrast, the RBF model provides more accurate prediction but its nonlinearity makes optimization nonlinear and non-convex, and therefore not always solvable. In this paper, the two models are combined so that linear optimization is enabled and the prediction accuracy is compensated, and therefore, both reliability and accuracy are achieved in a relative low computing cost. Using the developed method, the nonlinear MPC (NMPC) of the air/fuel ratio is implemented and evaluated by computer simulation. A real-time simulation using d-SPACE is also conducted to assess the real-time execution of the software. The simulation results show that the RBF compensated MPC outperformed over the Volterra model or RBF model based control. The dynamics of air manifold and fuel injection in spark ignition (SI) engines are modelled with both a modified Volterra series and a radial basis function (RBF) network. In a Volterra model-based model predictive control (MPC) the global optimal control can always be solved by a linear optimization, but the model accuracy is low. On contrast, the RBF model provides more accurate prediction but its nonlinearity makes optimization nonlinear and non-convex, and therefore not always solvable. In this paper, the two models are combined so that linear optimization is enabled and the prediction accuracy is compensated, and therefore, both reliability and accuracy are achieved in a relative low computing cost. Using the developed method, the nonlinear MPC (NMPC) of the air/fuel ratio is implemented and evaluated by computer simulation. A real-time simulation using d-SPACE is also conducted to assess the real-time execution of the software. The simulation results show that the RBF compensated MPC outperformed over the Volterra model or RBF model based control. RBF model Elsevier Nonlinear model predictive control Elsevier Air/fuel ratio control Elsevier SI engines Elsevier Volterra model Elsevier Yu, Ding-Li oth Tian, Yantao oth Shi, Yaowu oth Enthalten in Elsevier Science Liu, Xiang ELSEVIER Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation 2015 the international journal of real-time automation : a journal affiliated with IFAC, the International Federation of Automatic Control Amsterdam [u.a.] (DE-627)ELV013402978 volume:45 year:2015 pages:313-324 extent:12 https://doi.org/10.1016/j.engappai.2015.07.008 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.00 Medizin: Allgemeines VZ AR 45 2015 313-324 12 045F 004 |
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10.1016/j.engappai.2015.07.008 doi GBVA2015016000002.pica (DE-627)ELV039834603 (ELSEVIER)S0952-1976(15)00155-4 DE-627 ger DE-627 rakwb eng 004 004 DE-600 540 VZ 610 VZ 44.00 bkl Shi, Yiran verfasserin aut Air–fuel ratio prediction and NMPC for SI engines with modified Volterra model and RBF network 2015transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The dynamics of air manifold and fuel injection in spark ignition (SI) engines are modelled with both a modified Volterra series and a radial basis function (RBF) network. In a Volterra model-based model predictive control (MPC) the global optimal control can always be solved by a linear optimization, but the model accuracy is low. On contrast, the RBF model provides more accurate prediction but its nonlinearity makes optimization nonlinear and non-convex, and therefore not always solvable. In this paper, the two models are combined so that linear optimization is enabled and the prediction accuracy is compensated, and therefore, both reliability and accuracy are achieved in a relative low computing cost. Using the developed method, the nonlinear MPC (NMPC) of the air/fuel ratio is implemented and evaluated by computer simulation. A real-time simulation using d-SPACE is also conducted to assess the real-time execution of the software. The simulation results show that the RBF compensated MPC outperformed over the Volterra model or RBF model based control. The dynamics of air manifold and fuel injection in spark ignition (SI) engines are modelled with both a modified Volterra series and a radial basis function (RBF) network. In a Volterra model-based model predictive control (MPC) the global optimal control can always be solved by a linear optimization, but the model accuracy is low. On contrast, the RBF model provides more accurate prediction but its nonlinearity makes optimization nonlinear and non-convex, and therefore not always solvable. In this paper, the two models are combined so that linear optimization is enabled and the prediction accuracy is compensated, and therefore, both reliability and accuracy are achieved in a relative low computing cost. Using the developed method, the nonlinear MPC (NMPC) of the air/fuel ratio is implemented and evaluated by computer simulation. A real-time simulation using d-SPACE is also conducted to assess the real-time execution of the software. The simulation results show that the RBF compensated MPC outperformed over the Volterra model or RBF model based control. RBF model Elsevier Nonlinear model predictive control Elsevier Air/fuel ratio control Elsevier SI engines Elsevier Volterra model Elsevier Yu, Ding-Li oth Tian, Yantao oth Shi, Yaowu oth Enthalten in Elsevier Science Liu, Xiang ELSEVIER Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation 2015 the international journal of real-time automation : a journal affiliated with IFAC, the International Federation of Automatic Control Amsterdam [u.a.] (DE-627)ELV013402978 volume:45 year:2015 pages:313-324 extent:12 https://doi.org/10.1016/j.engappai.2015.07.008 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.00 Medizin: Allgemeines VZ AR 45 2015 313-324 12 045F 004 |
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10.1016/j.engappai.2015.07.008 doi GBVA2015016000002.pica (DE-627)ELV039834603 (ELSEVIER)S0952-1976(15)00155-4 DE-627 ger DE-627 rakwb eng 004 004 DE-600 540 VZ 610 VZ 44.00 bkl Shi, Yiran verfasserin aut Air–fuel ratio prediction and NMPC for SI engines with modified Volterra model and RBF network 2015transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The dynamics of air manifold and fuel injection in spark ignition (SI) engines are modelled with both a modified Volterra series and a radial basis function (RBF) network. In a Volterra model-based model predictive control (MPC) the global optimal control can always be solved by a linear optimization, but the model accuracy is low. On contrast, the RBF model provides more accurate prediction but its nonlinearity makes optimization nonlinear and non-convex, and therefore not always solvable. In this paper, the two models are combined so that linear optimization is enabled and the prediction accuracy is compensated, and therefore, both reliability and accuracy are achieved in a relative low computing cost. Using the developed method, the nonlinear MPC (NMPC) of the air/fuel ratio is implemented and evaluated by computer simulation. A real-time simulation using d-SPACE is also conducted to assess the real-time execution of the software. The simulation results show that the RBF compensated MPC outperformed over the Volterra model or RBF model based control. The dynamics of air manifold and fuel injection in spark ignition (SI) engines are modelled with both a modified Volterra series and a radial basis function (RBF) network. In a Volterra model-based model predictive control (MPC) the global optimal control can always be solved by a linear optimization, but the model accuracy is low. On contrast, the RBF model provides more accurate prediction but its nonlinearity makes optimization nonlinear and non-convex, and therefore not always solvable. In this paper, the two models are combined so that linear optimization is enabled and the prediction accuracy is compensated, and therefore, both reliability and accuracy are achieved in a relative low computing cost. Using the developed method, the nonlinear MPC (NMPC) of the air/fuel ratio is implemented and evaluated by computer simulation. A real-time simulation using d-SPACE is also conducted to assess the real-time execution of the software. The simulation results show that the RBF compensated MPC outperformed over the Volterra model or RBF model based control. RBF model Elsevier Nonlinear model predictive control Elsevier Air/fuel ratio control Elsevier SI engines Elsevier Volterra model Elsevier Yu, Ding-Li oth Tian, Yantao oth Shi, Yaowu oth Enthalten in Elsevier Science Liu, Xiang ELSEVIER Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation 2015 the international journal of real-time automation : a journal affiliated with IFAC, the International Federation of Automatic Control Amsterdam [u.a.] (DE-627)ELV013402978 volume:45 year:2015 pages:313-324 extent:12 https://doi.org/10.1016/j.engappai.2015.07.008 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.00 Medizin: Allgemeines VZ AR 45 2015 313-324 12 045F 004 |
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10.1016/j.engappai.2015.07.008 doi GBVA2015016000002.pica (DE-627)ELV039834603 (ELSEVIER)S0952-1976(15)00155-4 DE-627 ger DE-627 rakwb eng 004 004 DE-600 540 VZ 610 VZ 44.00 bkl Shi, Yiran verfasserin aut Air–fuel ratio prediction and NMPC for SI engines with modified Volterra model and RBF network 2015transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The dynamics of air manifold and fuel injection in spark ignition (SI) engines are modelled with both a modified Volterra series and a radial basis function (RBF) network. In a Volterra model-based model predictive control (MPC) the global optimal control can always be solved by a linear optimization, but the model accuracy is low. On contrast, the RBF model provides more accurate prediction but its nonlinearity makes optimization nonlinear and non-convex, and therefore not always solvable. In this paper, the two models are combined so that linear optimization is enabled and the prediction accuracy is compensated, and therefore, both reliability and accuracy are achieved in a relative low computing cost. Using the developed method, the nonlinear MPC (NMPC) of the air/fuel ratio is implemented and evaluated by computer simulation. A real-time simulation using d-SPACE is also conducted to assess the real-time execution of the software. The simulation results show that the RBF compensated MPC outperformed over the Volterra model or RBF model based control. The dynamics of air manifold and fuel injection in spark ignition (SI) engines are modelled with both a modified Volterra series and a radial basis function (RBF) network. In a Volterra model-based model predictive control (MPC) the global optimal control can always be solved by a linear optimization, but the model accuracy is low. On contrast, the RBF model provides more accurate prediction but its nonlinearity makes optimization nonlinear and non-convex, and therefore not always solvable. In this paper, the two models are combined so that linear optimization is enabled and the prediction accuracy is compensated, and therefore, both reliability and accuracy are achieved in a relative low computing cost. Using the developed method, the nonlinear MPC (NMPC) of the air/fuel ratio is implemented and evaluated by computer simulation. A real-time simulation using d-SPACE is also conducted to assess the real-time execution of the software. The simulation results show that the RBF compensated MPC outperformed over the Volterra model or RBF model based control. RBF model Elsevier Nonlinear model predictive control Elsevier Air/fuel ratio control Elsevier SI engines Elsevier Volterra model Elsevier Yu, Ding-Li oth Tian, Yantao oth Shi, Yaowu oth Enthalten in Elsevier Science Liu, Xiang ELSEVIER Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation 2015 the international journal of real-time automation : a journal affiliated with IFAC, the International Federation of Automatic Control Amsterdam [u.a.] (DE-627)ELV013402978 volume:45 year:2015 pages:313-324 extent:12 https://doi.org/10.1016/j.engappai.2015.07.008 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.00 Medizin: Allgemeines VZ AR 45 2015 313-324 12 045F 004 |
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Enthalten in Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation Amsterdam [u.a.] volume:45 year:2015 pages:313-324 extent:12 |
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Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation |
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In a Volterra model-based model predictive control (MPC) the global optimal control can always be solved by a linear optimization, but the model accuracy is low. On contrast, the RBF model provides more accurate prediction but its nonlinearity makes optimization nonlinear and non-convex, and therefore not always solvable. In this paper, the two models are combined so that linear optimization is enabled and the prediction accuracy is compensated, and therefore, both reliability and accuracy are achieved in a relative low computing cost. Using the developed method, the nonlinear MPC (NMPC) of the air/fuel ratio is implemented and evaluated by computer simulation. A real-time simulation using d-SPACE is also conducted to assess the real-time execution of the software. 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air–fuel ratio prediction and nmpc for si engines with modified volterra model and rbf network |
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Air–fuel ratio prediction and NMPC for SI engines with modified Volterra model and RBF network |
| abstract |
The dynamics of air manifold and fuel injection in spark ignition (SI) engines are modelled with both a modified Volterra series and a radial basis function (RBF) network. In a Volterra model-based model predictive control (MPC) the global optimal control can always be solved by a linear optimization, but the model accuracy is low. On contrast, the RBF model provides more accurate prediction but its nonlinearity makes optimization nonlinear and non-convex, and therefore not always solvable. In this paper, the two models are combined so that linear optimization is enabled and the prediction accuracy is compensated, and therefore, both reliability and accuracy are achieved in a relative low computing cost. Using the developed method, the nonlinear MPC (NMPC) of the air/fuel ratio is implemented and evaluated by computer simulation. A real-time simulation using d-SPACE is also conducted to assess the real-time execution of the software. The simulation results show that the RBF compensated MPC outperformed over the Volterra model or RBF model based control. |
| abstractGer |
The dynamics of air manifold and fuel injection in spark ignition (SI) engines are modelled with both a modified Volterra series and a radial basis function (RBF) network. In a Volterra model-based model predictive control (MPC) the global optimal control can always be solved by a linear optimization, but the model accuracy is low. On contrast, the RBF model provides more accurate prediction but its nonlinearity makes optimization nonlinear and non-convex, and therefore not always solvable. In this paper, the two models are combined so that linear optimization is enabled and the prediction accuracy is compensated, and therefore, both reliability and accuracy are achieved in a relative low computing cost. Using the developed method, the nonlinear MPC (NMPC) of the air/fuel ratio is implemented and evaluated by computer simulation. A real-time simulation using d-SPACE is also conducted to assess the real-time execution of the software. The simulation results show that the RBF compensated MPC outperformed over the Volterra model or RBF model based control. |
| abstract_unstemmed |
The dynamics of air manifold and fuel injection in spark ignition (SI) engines are modelled with both a modified Volterra series and a radial basis function (RBF) network. In a Volterra model-based model predictive control (MPC) the global optimal control can always be solved by a linear optimization, but the model accuracy is low. On contrast, the RBF model provides more accurate prediction but its nonlinearity makes optimization nonlinear and non-convex, and therefore not always solvable. In this paper, the two models are combined so that linear optimization is enabled and the prediction accuracy is compensated, and therefore, both reliability and accuracy are achieved in a relative low computing cost. Using the developed method, the nonlinear MPC (NMPC) of the air/fuel ratio is implemented and evaluated by computer simulation. A real-time simulation using d-SPACE is also conducted to assess the real-time execution of the software. The simulation results show that the RBF compensated MPC outperformed over the Volterra model or RBF model based control. |
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Air–fuel ratio prediction and NMPC for SI engines with modified Volterra model and RBF network |
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