Efficient Nonlinear Model Predictive Control of Automated Vehicles
In this paper, an efficient model predictive control (MPC) of velocity tracking of automated vehicles is proposed, in which a reference signal is given <i>a priori</i>. Five degree-of-freedom vehicle dynamics with nonlinear tires is chosen as the prediction model, in which coupling chara...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/10/21/4163 |
| _version_ | 1797467331459284992 |
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| author | Shuyou Yu Encong Sheng Yajing Zhang Yongfu Li Hong Chen Yi Hao |
| author_facet | Shuyou Yu Encong Sheng Yajing Zhang Yongfu Li Hong Chen Yi Hao |
| author_sort | Shuyou Yu |
| collection | DOAJ |
| description | In this paper, an efficient model predictive control (MPC) of velocity tracking of automated vehicles is proposed, in which a reference signal is given <i>a priori</i>. Five degree-of-freedom vehicle dynamics with nonlinear tires is chosen as the prediction model, in which coupling characteristics of longitudinal and lateral dynamics are taken into account. In order to balance computational burden and prediction accuracy, Koopman operator theory is adopted to transform the nonlinear model into a global linear model. Then, the global linear model is used in the design of MPC to reduce online computational burden and avoid solving nonconvex/nonlinear optimization problems. Furthermore, the effectiveness of Koopman operator in vehicle dynamics control is verified using a Matlab/Simulink environment. Validation results demonstrate that dynamic mode decomposition with control (DMDc) and extended dynamic mode decomposition (EDMD) algorithms are more accurate in model validation and dynamic prediction than local linearization, and DMDc algorithm has less computational burden on solving optimization problems than the EDMD algorithm. |
| first_indexed | 2024-03-09T18:52:06Z |
| format | Article |
| id | doaj.art-a0ae64f83c8d47229c86ebce2c847a41 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T18:52:06Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-a0ae64f83c8d47229c86ebce2c847a412023-11-24T05:45:52ZengMDPI AGMathematics2227-73902022-11-011021416310.3390/math10214163Efficient Nonlinear Model Predictive Control of Automated VehiclesShuyou Yu0Encong Sheng1Yajing Zhang2Yongfu Li3Hong Chen4Yi Hao5State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, ChinaDepartment of Control Science and Engineering, Jilin University, Changchun 130022, ChinaDepartment of Control Science and Engineering, Jilin University, Changchun 130022, ChinaDepartment of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaDepartment of Control Science and Engineering, Jilin University, Changchun 130022, ChinaDongfeng Commercial Vehicle Technology Center, Dongfeng Motor Corporation, Wuhan 442001, ChinaIn this paper, an efficient model predictive control (MPC) of velocity tracking of automated vehicles is proposed, in which a reference signal is given <i>a priori</i>. Five degree-of-freedom vehicle dynamics with nonlinear tires is chosen as the prediction model, in which coupling characteristics of longitudinal and lateral dynamics are taken into account. In order to balance computational burden and prediction accuracy, Koopman operator theory is adopted to transform the nonlinear model into a global linear model. Then, the global linear model is used in the design of MPC to reduce online computational burden and avoid solving nonconvex/nonlinear optimization problems. Furthermore, the effectiveness of Koopman operator in vehicle dynamics control is verified using a Matlab/Simulink environment. Validation results demonstrate that dynamic mode decomposition with control (DMDc) and extended dynamic mode decomposition (EDMD) algorithms are more accurate in model validation and dynamic prediction than local linearization, and DMDc algorithm has less computational burden on solving optimization problems than the EDMD algorithm.https://www.mdpi.com/2227-7390/10/21/4163automated vehicle controlnonlinear model predictive controlKoopman operatordata-driven control |
| spellingShingle | Shuyou Yu Encong Sheng Yajing Zhang Yongfu Li Hong Chen Yi Hao Efficient Nonlinear Model Predictive Control of Automated Vehicles Mathematics automated vehicle control nonlinear model predictive control Koopman operator data-driven control |
| title | Efficient Nonlinear Model Predictive Control of Automated Vehicles |
| title_full | Efficient Nonlinear Model Predictive Control of Automated Vehicles |
| title_fullStr | Efficient Nonlinear Model Predictive Control of Automated Vehicles |
| title_full_unstemmed | Efficient Nonlinear Model Predictive Control of Automated Vehicles |
| title_short | Efficient Nonlinear Model Predictive Control of Automated Vehicles |
| title_sort | efficient nonlinear model predictive control of automated vehicles |
| topic | automated vehicle control nonlinear model predictive control Koopman operator data-driven control |
| url | https://www.mdpi.com/2227-7390/10/21/4163 |
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