Fast Distributed Model Predictive Control Method for Active Suspension Systems
In order to balance the performance index and computational efficiency of the active suspension control system, this paper offers a fast distributed model predictive control (DMPC) method based on multi-agents for the active suspension system. Firstly, a seven-degrees-of-freedom model of the vehicle...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/6/3357 |
| _version_ | 1797608957729046528 |
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| author | Niaona Zhang Sheng Yang Guangyi Wu Haitao Ding Zhe Zhang Konghui Guo |
| author_facet | Niaona Zhang Sheng Yang Guangyi Wu Haitao Ding Zhe Zhang Konghui Guo |
| author_sort | Niaona Zhang |
| collection | DOAJ |
| description | In order to balance the performance index and computational efficiency of the active suspension control system, this paper offers a fast distributed model predictive control (DMPC) method based on multi-agents for the active suspension system. Firstly, a seven-degrees-of-freedom model of the vehicle is created. This study establishes a reduced-dimension vehicle model based on graph theory in accordance with its network topology and mutual coupling constraints. Then, for engineering applications, a multi-agent-based distributed model predictive control method of an active suspension system is presented. The partial differential equation of rolling optimization is solved by a radical basis function (RBF) neural network. It improves the computational efficiency of the algorithm on the premise of satisfying multi-objective optimization. Finally, the joint simulation of CarSim and Matlab/Simulink shows that the control system can greatly minimize the vertical acceleration, pitch acceleration, and roll acceleration of the vehicle body. In particular, under the steering condition, it can take into account the safety, comfort, and handling stability of the vehicle at the same time. |
| first_indexed | 2024-03-11T05:54:57Z |
| format | Article |
| id | doaj.art-07839a57ec3a47868aaddc3712556906 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T05:54:57Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-07839a57ec3a47868aaddc37125569062023-11-17T13:49:34ZengMDPI AGSensors1424-82202023-03-01236335710.3390/s23063357Fast Distributed Model Predictive Control Method for Active Suspension SystemsNiaona Zhang0Sheng Yang1Guangyi Wu2Haitao Ding3Zhe Zhang4Konghui Guo5School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130012, ChinaSchool of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130012, ChinaSchool of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130012, ChinaState Key Laboratory of Automobile Simulation and Control, Jilin University, Changchun 130012, ChinaState Key Laboratory of Automobile Simulation and Control, Jilin University, Changchun 130012, ChinaState Key Laboratory of Automobile Simulation and Control, Jilin University, Changchun 130012, ChinaIn order to balance the performance index and computational efficiency of the active suspension control system, this paper offers a fast distributed model predictive control (DMPC) method based on multi-agents for the active suspension system. Firstly, a seven-degrees-of-freedom model of the vehicle is created. This study establishes a reduced-dimension vehicle model based on graph theory in accordance with its network topology and mutual coupling constraints. Then, for engineering applications, a multi-agent-based distributed model predictive control method of an active suspension system is presented. The partial differential equation of rolling optimization is solved by a radical basis function (RBF) neural network. It improves the computational efficiency of the algorithm on the premise of satisfying multi-objective optimization. Finally, the joint simulation of CarSim and Matlab/Simulink shows that the control system can greatly minimize the vertical acceleration, pitch acceleration, and roll acceleration of the vehicle body. In particular, under the steering condition, it can take into account the safety, comfort, and handling stability of the vehicle at the same time.https://www.mdpi.com/1424-8220/23/6/3357active suspension systemdistributed model predictive controlmulti-agentRBF neural network |
| spellingShingle | Niaona Zhang Sheng Yang Guangyi Wu Haitao Ding Zhe Zhang Konghui Guo Fast Distributed Model Predictive Control Method for Active Suspension Systems Sensors active suspension system distributed model predictive control multi-agent RBF neural network |
| title | Fast Distributed Model Predictive Control Method for Active Suspension Systems |
| title_full | Fast Distributed Model Predictive Control Method for Active Suspension Systems |
| title_fullStr | Fast Distributed Model Predictive Control Method for Active Suspension Systems |
| title_full_unstemmed | Fast Distributed Model Predictive Control Method for Active Suspension Systems |
| title_short | Fast Distributed Model Predictive Control Method for Active Suspension Systems |
| title_sort | fast distributed model predictive control method for active suspension systems |
| topic | active suspension system distributed model predictive control multi-agent RBF neural network |
| url | https://www.mdpi.com/1424-8220/23/6/3357 |
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