Hierarchical Nonlinear Model Predictive Control for an Autonomous Racecar
2021 20th International Conference on Advanced Robotics (ICAR)(2021)
摘要
This paper presents an optimization-based hierarchical motion planning and control architecture for autonomous racing that enables fast feedback times without significantly compromising on model accuracy. The online control scheme is divided into a high level NMPC for time-optimal trajectory planning and a low level NMPC for trajectory tracking using the same vehicle model. We numerically validate our approach in terms of computational efficiency and closed-loop performance in a detailed vehicle dynamics simulation environment. Despite considerable model-plant mismatches, the proposed hierarchical NMPC controller achieves a driven lap time in simulation which is only marginally slower than the theoretically optimal trajectory calculated offline via Optimal Control.
更多查看译文
关键词
Robust Control,Optimization,Real-time Optimization,Constraint Handling,Real-Time Simulation
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要