NMPC-Based Path Tracking Control Through Cascaded Discretization Method Considering Handling Stability for 4WS Autonomous Vehicles Under Extreme Conditions
In the realm of autonomous driving, motion control generally involves two critical aspects—path following and stability control—and an inevitable mutual interference exists between them under extreme conditions. To tackle this challenge, this study proposes a collaborative approach of path tracking...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
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| Series: | World Electric Vehicle Journal |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2032-6653/15/12/573 |
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| Summary: | In the realm of autonomous driving, motion control generally involves two critical aspects—path following and stability control—and an inevitable mutual interference exists between them under extreme conditions. To tackle this challenge, this study proposes a collaborative approach of path tracking and stability control. When designing the path tracking control module, the effects of vertical tire load transfer and road surface adhesion coefficients on tire force calculations were taken into account to mitigate vehicle dynamics model mismatch. Leveraging the receding horizon optimization characteristic of nonlinear model predictive control (NMPC), a cascaded discretization approach was utilized to realize a balance between precision and real-time performance in numerical solutions. Then, a stability controller, which employs rear wheel steering, was designed to prevent excessive increases in the vehicle’s sideslip angle, thereby ensuring the vehicle’s lateral stability. The effectiveness of the proposed strategy is validated through CarSim 8.0/Simulink cosimulation. The outcomes demonstrate that the stability controller significantly enhances vehicle stability under high-speed and low-adhesion conditions. On the premise of stability, the proposed path tracking controller has exhibited significant enhancements in real-time performance, without compromising the accuracy of path tracking. |
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| ISSN: | 2032-6653 |