Integrated Adaptive Cruise Control with Weight Coefficient Self-Tuning Strategy
This paper presents a novel multi-objective coordinated adaptive cruise control (ACC) algorithm based on a model predictive control (MPC) framework which can comprehensively address issues regarding longitudinal car-following performance, lateral stability, as well as vehicle safety. During the car-...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2018-06-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2076-3417/8/6/978 |
| _version_ | 1819015653877612544 |
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| author | Junhui Zhang Qing Li Dapeng Chen |
| author_facet | Junhui Zhang Qing Li Dapeng Chen |
| author_sort | Junhui Zhang |
| collection | DOAJ |
| description | This paper presents a novel multi-objective coordinated adaptive cruise control (ACC) algorithm based on a model predictive control (MPC) framework which can comprehensively address issues regarding longitudinal car-following performance, lateral stability, as well as vehicle safety. During the car-following, vehicle dynamics, illustrating the forces acting on the tire contact patches, are established. To simplify the tightly coupled dynamics system, a state-feedback based disturbance decoupling method is employed, by which longitudinal and lateral dynamics can be completely decoupled. Furthermore, the traditional MPC control with a constant weight matrix will probably not be able to solve time-varying multi-objective coordinated optimization issues, especially in transient scenarios. A weight coefficient self-tuning strategy is therefore suggested by which the weight coefficient for each sub-objective can be adjusted automatically with the change of traffic scenarios, accordingly improving the overall car-following performance. The simulations show that the control algorithm utilizing the suggested self-tuning strategy reaps significant benefits in terms of longitudinal car-following performance, while at the same time maintaining a small lateral stability error range. |
| first_indexed | 2024-12-21T02:35:10Z |
| format | Article |
| id | doaj.art-39a6f9e59c18457ab9913c62c6ea79e3 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-21T02:35:10Z |
| publishDate | 2018-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-39a6f9e59c18457ab9913c62c6ea79e32022-12-21T19:18:50ZengMDPI AGApplied Sciences2076-34172018-06-018697810.3390/app8060978app8060978Integrated Adaptive Cruise Control with Weight Coefficient Self-Tuning StrategyJunhui Zhang0Qing Li1Dapeng Chen2Automotive Electronics Center, Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, ChinaAutomotive Electronics Center, Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, ChinaAutomotive Electronics Center, Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, ChinaThis paper presents a novel multi-objective coordinated adaptive cruise control (ACC) algorithm based on a model predictive control (MPC) framework which can comprehensively address issues regarding longitudinal car-following performance, lateral stability, as well as vehicle safety. During the car-following, vehicle dynamics, illustrating the forces acting on the tire contact patches, are established. To simplify the tightly coupled dynamics system, a state-feedback based disturbance decoupling method is employed, by which longitudinal and lateral dynamics can be completely decoupled. Furthermore, the traditional MPC control with a constant weight matrix will probably not be able to solve time-varying multi-objective coordinated optimization issues, especially in transient scenarios. A weight coefficient self-tuning strategy is therefore suggested by which the weight coefficient for each sub-objective can be adjusted automatically with the change of traffic scenarios, accordingly improving the overall car-following performance. The simulations show that the control algorithm utilizing the suggested self-tuning strategy reaps significant benefits in terms of longitudinal car-following performance, while at the same time maintaining a small lateral stability error range.http://www.mdpi.com/2076-3417/8/6/978adaptive cruise control (ACC)model predictive control (MPC)direct yaw-moment control (DYC)longitudinal car-following performancelateral stability |
| spellingShingle | Junhui Zhang Qing Li Dapeng Chen Integrated Adaptive Cruise Control with Weight Coefficient Self-Tuning Strategy Applied Sciences adaptive cruise control (ACC) model predictive control (MPC) direct yaw-moment control (DYC) longitudinal car-following performance lateral stability |
| title | Integrated Adaptive Cruise Control with Weight Coefficient Self-Tuning Strategy |
| title_full | Integrated Adaptive Cruise Control with Weight Coefficient Self-Tuning Strategy |
| title_fullStr | Integrated Adaptive Cruise Control with Weight Coefficient Self-Tuning Strategy |
| title_full_unstemmed | Integrated Adaptive Cruise Control with Weight Coefficient Self-Tuning Strategy |
| title_short | Integrated Adaptive Cruise Control with Weight Coefficient Self-Tuning Strategy |
| title_sort | integrated adaptive cruise control with weight coefficient self tuning strategy |
| topic | adaptive cruise control (ACC) model predictive control (MPC) direct yaw-moment control (DYC) longitudinal car-following performance lateral stability |
| url | http://www.mdpi.com/2076-3417/8/6/978 |
| work_keys_str_mv | AT junhuizhang integratedadaptivecruisecontrolwithweightcoefficientselftuningstrategy AT qingli integratedadaptivecruisecontrolwithweightcoefficientselftuningstrategy AT dapengchen integratedadaptivecruisecontrolwithweightcoefficientselftuningstrategy |