Distributed model predictive control for active air suspension systems of autonomous vehicles
This work examines the Distributed Model Predictive Control (DMPC) strategy for an intelligent car air spring suspension system. The nonlinear properties and complexity of air spring suspension are crucial for enhancing handling stability and ride comfort, although they also provide control challeng...
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2025
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sg-ntu-dr.10356-1824282025-02-08T16:55:33Z Distributed model predictive control for active air suspension systems of autonomous vehicles Wang, Muyun Yifan Wang School of Mechanical and Aerospace Engineering yifan.wang@ntu.edu.sg Engineering This work examines the Distributed Model Predictive Control (DMPC) strategy for an intelligent car air spring suspension system. The nonlinear properties and complexity of air spring suspension are crucial for enhancing handling stability and ride comfort, although they also provide control challenges for suspension systems. To get comprehensive performance enhancement, this study adopts a DMPC technique that breaks down the vehicle suspension system into many subsystems and optimizes each subsystem's control strategy collaboratively. The proposed method effectively maintains system response time and control accuracy while also improving these parameters. The simulation findings indicate that the DMPC control algorithm enhanced the performance of the connected suspension relative to the passive suspension and the traditional MPC method. which would increase the vehicle's handling stability and comfort. Master's degree 2025-02-03T01:00:43Z 2025-02-03T01:00:43Z 2024 Thesis-Master by Coursework Wang, M. (2024). Distributed model predictive control for active air suspension systems of autonomous vehicles. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/182428 https://hdl.handle.net/10356/182428 en application/pdf Nanyang Technological University |
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This work examines the Distributed Model Predictive Control (DMPC) strategy for an intelligent car air spring suspension system. The nonlinear properties and complexity of air spring suspension are crucial for enhancing handling stability and ride comfort, although they also provide control challenges for suspension systems. To get comprehensive performance enhancement, this study adopts a DMPC technique that breaks down the vehicle suspension system into many subsystems and optimizes each subsystem's control strategy collaboratively. The proposed method effectively maintains system response time and control accuracy while also improving these parameters. The simulation findings indicate that the DMPC control algorithm enhanced the performance of the connected suspension relative to the passive suspension and the traditional MPC method. which would increase the vehicle's handling stability and comfort. |
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Yifan Wang |
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Yifan Wang Wang, Muyun |
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Thesis-Master by Coursework |
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Wang, Muyun |
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Wang, Muyun |
title |
Distributed model predictive control for active air suspension systems of autonomous vehicles |
title_short |
Distributed model predictive control for active air suspension systems of autonomous vehicles |
title_full |
Distributed model predictive control for active air suspension systems of autonomous vehicles |
title_fullStr |
Distributed model predictive control for active air suspension systems of autonomous vehicles |
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Distributed model predictive control for active air suspension systems of autonomous vehicles |
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distributed model predictive control for active air suspension systems of autonomous vehicles |
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Nanyang Technological University |
publishDate |
2025 |
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https://hdl.handle.net/10356/182428 |
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1823807379451412480 |