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...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis-Master by Coursework |
| Language: | English |
| Published: |
Nanyang Technological University
2025
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/182428 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | 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. |
|---|