Extended model predictive control scheme for smooth path following of autonomous vehicles
Abstract This paper presents an extended model predictive control (MPC) scheme for implementing optimal path following of autonomous vehicles, which has multiple constraints and an integrated model of vehicle and road dynamics. Road curvature and inclination factors are used in the construction of t...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Liu, Qianjie [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© Higher Education Press 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Frontiers of mechanical engineering in China - Berlin : Heidelberg : Springer, 2006, 17(2022), 1 vom: März |
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| Übergeordnetes Werk: |
volume:17 ; year:2022 ; number:1 ; month:03 |
| Links: |
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| DOI / URN: |
10.1007/s11465-021-0660-4 |
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| 520 | |a Abstract This paper presents an extended model predictive control (MPC) scheme for implementing optimal path following of autonomous vehicles, which has multiple constraints and an integrated model of vehicle and road dynamics. Road curvature and inclination factors are used in the construction of the vehicle dynamic model to describe its lateral and roll dynamics accurately. Sideslip, rollover, and vehicle envelopes are used as multiple constraints in the MPC controller formulation. Then, an extended MPC method solved by differential evolution optimization algorithm is proposed to realize optimal smooth path following based on driving path features. Finally, simulation and real experiments are carried out to evaluate the feasibility and the effectiveness of the extended MPC scheme. Results indicate that the proposed method can obtain the smooth transition to follow the optimal drivable path and satisfy the lateral dynamic stability and environmental constraints, which can improve the path following quality for better ride comfort and road availability of autonomous vehicles. | ||
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| 650 | 4 | |a vehicle dynamic modeling |7 (dpeaa)DE-He213 | |
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| 650 | 4 | |a path following |7 (dpeaa)DE-He213 | |
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| 700 | 1 | |a Zhu, Qingyuan |4 aut | |
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10.1007/s11465-021-0660-4 doi (DE-627)SPR050569619 (SPR)s11465-021-0660-4-e DE-627 ger DE-627 rakwb eng Liu, Qianjie verfasserin aut Extended model predictive control scheme for smooth path following of autonomous vehicles 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Higher Education Press 2022 Abstract This paper presents an extended model predictive control (MPC) scheme for implementing optimal path following of autonomous vehicles, which has multiple constraints and an integrated model of vehicle and road dynamics. Road curvature and inclination factors are used in the construction of the vehicle dynamic model to describe its lateral and roll dynamics accurately. Sideslip, rollover, and vehicle envelopes are used as multiple constraints in the MPC controller formulation. Then, an extended MPC method solved by differential evolution optimization algorithm is proposed to realize optimal smooth path following based on driving path features. Finally, simulation and real experiments are carried out to evaluate the feasibility and the effectiveness of the extended MPC scheme. Results indicate that the proposed method can obtain the smooth transition to follow the optimal drivable path and satisfy the lateral dynamic stability and environmental constraints, which can improve the path following quality for better ride comfort and road availability of autonomous vehicles. autonomous vehicles (dpeaa)DE-He213 vehicle dynamic modeling (dpeaa)DE-He213 model predictive control (dpeaa)DE-He213 path following (dpeaa)DE-He213 optimization algorithm (dpeaa)DE-He213 Song, Shuang aut Hu, Huosheng aut Huang, Tengchao aut Li, Chenyang aut Zhu, Qingyuan aut Enthalten in Frontiers of mechanical engineering in China Berlin : Heidelberg : Springer, 2006 17(2022), 1 vom: März (DE-627)510464319 (DE-600)2230609-2 1673-3592 nnns volume:17 year:2022 number:1 month:03 https://dx.doi.org/10.1007/s11465-021-0660-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 AR 17 2022 1 03 |
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10.1007/s11465-021-0660-4 doi (DE-627)SPR050569619 (SPR)s11465-021-0660-4-e DE-627 ger DE-627 rakwb eng Liu, Qianjie verfasserin aut Extended model predictive control scheme for smooth path following of autonomous vehicles 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Higher Education Press 2022 Abstract This paper presents an extended model predictive control (MPC) scheme for implementing optimal path following of autonomous vehicles, which has multiple constraints and an integrated model of vehicle and road dynamics. Road curvature and inclination factors are used in the construction of the vehicle dynamic model to describe its lateral and roll dynamics accurately. Sideslip, rollover, and vehicle envelopes are used as multiple constraints in the MPC controller formulation. Then, an extended MPC method solved by differential evolution optimization algorithm is proposed to realize optimal smooth path following based on driving path features. Finally, simulation and real experiments are carried out to evaluate the feasibility and the effectiveness of the extended MPC scheme. Results indicate that the proposed method can obtain the smooth transition to follow the optimal drivable path and satisfy the lateral dynamic stability and environmental constraints, which can improve the path following quality for better ride comfort and road availability of autonomous vehicles. autonomous vehicles (dpeaa)DE-He213 vehicle dynamic modeling (dpeaa)DE-He213 model predictive control (dpeaa)DE-He213 path following (dpeaa)DE-He213 optimization algorithm (dpeaa)DE-He213 Song, Shuang aut Hu, Huosheng aut Huang, Tengchao aut Li, Chenyang aut Zhu, Qingyuan aut Enthalten in Frontiers of mechanical engineering in China Berlin : Heidelberg : Springer, 2006 17(2022), 1 vom: März (DE-627)510464319 (DE-600)2230609-2 1673-3592 nnns volume:17 year:2022 number:1 month:03 https://dx.doi.org/10.1007/s11465-021-0660-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 AR 17 2022 1 03 |
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10.1007/s11465-021-0660-4 doi (DE-627)SPR050569619 (SPR)s11465-021-0660-4-e DE-627 ger DE-627 rakwb eng Liu, Qianjie verfasserin aut Extended model predictive control scheme for smooth path following of autonomous vehicles 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Higher Education Press 2022 Abstract This paper presents an extended model predictive control (MPC) scheme for implementing optimal path following of autonomous vehicles, which has multiple constraints and an integrated model of vehicle and road dynamics. Road curvature and inclination factors are used in the construction of the vehicle dynamic model to describe its lateral and roll dynamics accurately. Sideslip, rollover, and vehicle envelopes are used as multiple constraints in the MPC controller formulation. Then, an extended MPC method solved by differential evolution optimization algorithm is proposed to realize optimal smooth path following based on driving path features. Finally, simulation and real experiments are carried out to evaluate the feasibility and the effectiveness of the extended MPC scheme. Results indicate that the proposed method can obtain the smooth transition to follow the optimal drivable path and satisfy the lateral dynamic stability and environmental constraints, which can improve the path following quality for better ride comfort and road availability of autonomous vehicles. autonomous vehicles (dpeaa)DE-He213 vehicle dynamic modeling (dpeaa)DE-He213 model predictive control (dpeaa)DE-He213 path following (dpeaa)DE-He213 optimization algorithm (dpeaa)DE-He213 Song, Shuang aut Hu, Huosheng aut Huang, Tengchao aut Li, Chenyang aut Zhu, Qingyuan aut Enthalten in Frontiers of mechanical engineering in China Berlin : Heidelberg : Springer, 2006 17(2022), 1 vom: März (DE-627)510464319 (DE-600)2230609-2 1673-3592 nnns volume:17 year:2022 number:1 month:03 https://dx.doi.org/10.1007/s11465-021-0660-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 AR 17 2022 1 03 |
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10.1007/s11465-021-0660-4 doi (DE-627)SPR050569619 (SPR)s11465-021-0660-4-e DE-627 ger DE-627 rakwb eng Liu, Qianjie verfasserin aut Extended model predictive control scheme for smooth path following of autonomous vehicles 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Higher Education Press 2022 Abstract This paper presents an extended model predictive control (MPC) scheme for implementing optimal path following of autonomous vehicles, which has multiple constraints and an integrated model of vehicle and road dynamics. Road curvature and inclination factors are used in the construction of the vehicle dynamic model to describe its lateral and roll dynamics accurately. Sideslip, rollover, and vehicle envelopes are used as multiple constraints in the MPC controller formulation. Then, an extended MPC method solved by differential evolution optimization algorithm is proposed to realize optimal smooth path following based on driving path features. Finally, simulation and real experiments are carried out to evaluate the feasibility and the effectiveness of the extended MPC scheme. Results indicate that the proposed method can obtain the smooth transition to follow the optimal drivable path and satisfy the lateral dynamic stability and environmental constraints, which can improve the path following quality for better ride comfort and road availability of autonomous vehicles. autonomous vehicles (dpeaa)DE-He213 vehicle dynamic modeling (dpeaa)DE-He213 model predictive control (dpeaa)DE-He213 path following (dpeaa)DE-He213 optimization algorithm (dpeaa)DE-He213 Song, Shuang aut Hu, Huosheng aut Huang, Tengchao aut Li, Chenyang aut Zhu, Qingyuan aut Enthalten in Frontiers of mechanical engineering in China Berlin : Heidelberg : Springer, 2006 17(2022), 1 vom: März (DE-627)510464319 (DE-600)2230609-2 1673-3592 nnns volume:17 year:2022 number:1 month:03 https://dx.doi.org/10.1007/s11465-021-0660-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 AR 17 2022 1 03 |
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10.1007/s11465-021-0660-4 doi (DE-627)SPR050569619 (SPR)s11465-021-0660-4-e DE-627 ger DE-627 rakwb eng Liu, Qianjie verfasserin aut Extended model predictive control scheme for smooth path following of autonomous vehicles 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Higher Education Press 2022 Abstract This paper presents an extended model predictive control (MPC) scheme for implementing optimal path following of autonomous vehicles, which has multiple constraints and an integrated model of vehicle and road dynamics. Road curvature and inclination factors are used in the construction of the vehicle dynamic model to describe its lateral and roll dynamics accurately. Sideslip, rollover, and vehicle envelopes are used as multiple constraints in the MPC controller formulation. Then, an extended MPC method solved by differential evolution optimization algorithm is proposed to realize optimal smooth path following based on driving path features. Finally, simulation and real experiments are carried out to evaluate the feasibility and the effectiveness of the extended MPC scheme. Results indicate that the proposed method can obtain the smooth transition to follow the optimal drivable path and satisfy the lateral dynamic stability and environmental constraints, which can improve the path following quality for better ride comfort and road availability of autonomous vehicles. autonomous vehicles (dpeaa)DE-He213 vehicle dynamic modeling (dpeaa)DE-He213 model predictive control (dpeaa)DE-He213 path following (dpeaa)DE-He213 optimization algorithm (dpeaa)DE-He213 Song, Shuang aut Hu, Huosheng aut Huang, Tengchao aut Li, Chenyang aut Zhu, Qingyuan aut Enthalten in Frontiers of mechanical engineering in China Berlin : Heidelberg : Springer, 2006 17(2022), 1 vom: März (DE-627)510464319 (DE-600)2230609-2 1673-3592 nnns volume:17 year:2022 number:1 month:03 https://dx.doi.org/10.1007/s11465-021-0660-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 AR 17 2022 1 03 |
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extended model predictive control scheme for smooth path following of autonomous vehicles |
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Extended model predictive control scheme for smooth path following of autonomous vehicles |
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Abstract This paper presents an extended model predictive control (MPC) scheme for implementing optimal path following of autonomous vehicles, which has multiple constraints and an integrated model of vehicle and road dynamics. Road curvature and inclination factors are used in the construction of the vehicle dynamic model to describe its lateral and roll dynamics accurately. Sideslip, rollover, and vehicle envelopes are used as multiple constraints in the MPC controller formulation. Then, an extended MPC method solved by differential evolution optimization algorithm is proposed to realize optimal smooth path following based on driving path features. Finally, simulation and real experiments are carried out to evaluate the feasibility and the effectiveness of the extended MPC scheme. Results indicate that the proposed method can obtain the smooth transition to follow the optimal drivable path and satisfy the lateral dynamic stability and environmental constraints, which can improve the path following quality for better ride comfort and road availability of autonomous vehicles. © Higher Education Press 2022 |
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Abstract This paper presents an extended model predictive control (MPC) scheme for implementing optimal path following of autonomous vehicles, which has multiple constraints and an integrated model of vehicle and road dynamics. Road curvature and inclination factors are used in the construction of the vehicle dynamic model to describe its lateral and roll dynamics accurately. Sideslip, rollover, and vehicle envelopes are used as multiple constraints in the MPC controller formulation. Then, an extended MPC method solved by differential evolution optimization algorithm is proposed to realize optimal smooth path following based on driving path features. Finally, simulation and real experiments are carried out to evaluate the feasibility and the effectiveness of the extended MPC scheme. Results indicate that the proposed method can obtain the smooth transition to follow the optimal drivable path and satisfy the lateral dynamic stability and environmental constraints, which can improve the path following quality for better ride comfort and road availability of autonomous vehicles. © Higher Education Press 2022 |
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Abstract This paper presents an extended model predictive control (MPC) scheme for implementing optimal path following of autonomous vehicles, which has multiple constraints and an integrated model of vehicle and road dynamics. Road curvature and inclination factors are used in the construction of the vehicle dynamic model to describe its lateral and roll dynamics accurately. Sideslip, rollover, and vehicle envelopes are used as multiple constraints in the MPC controller formulation. Then, an extended MPC method solved by differential evolution optimization algorithm is proposed to realize optimal smooth path following based on driving path features. Finally, simulation and real experiments are carried out to evaluate the feasibility and the effectiveness of the extended MPC scheme. Results indicate that the proposed method can obtain the smooth transition to follow the optimal drivable path and satisfy the lateral dynamic stability and environmental constraints, which can improve the path following quality for better ride comfort and road availability of autonomous vehicles. © Higher Education Press 2022 |
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