发表机构
Shanghai Jiaotong University; Bosch Corporate Research（上海交通大学; 博世公司研究部）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对车辆动力学控制痛点，开发了预测安全滤波器增强的课程学习控制器，经Python-CarSim验证，其在多操纵工况下性能提升与可扩展性更优。
AI 中文摘要
基于学习的控制技术近期取得显著进展，在多领域复杂控制问题中表现出色，但该方法无法保证安全性，因此在维持良好性能的同时提升安全性与鲁棒性至关重要。以车辆运动与动力学控制为例，为克服传统方法参数标定工作量大的痛点，同时让基于学习的控制在基于状态的车辆控制任务中，于稳定性与敏捷性方面较现有工作实现更好的性能与效率，本研究开发了一种由基于物理的预测安全滤波器增强的课程学习控制器。通过Python-CarSim平台开展验证，结果表明该控制器在多种操纵工况下具备更优的性能提升与可扩展性。
英文摘要
Recent advances in learning-based control have enabled impressive achievements in solving complex control problems in various domains. However, since learning-based control may not be able to realize safety-guaranties, it is of great importance to enhance safety and robustness while maintaining good performances. Take vehicle motion \& dynamics control as an example, in order to overcome the pain points of traditional methods such as heavy parameter calibration effort and learning-based control to bring better performance and efficiency in stability \& agility over prior work for state-based vehicle control tasks, in this work, our method aims to develop a curriculum learning controller enhanced with physics-based predictive safety filter. The validation is conducted with the Python-CarSim platform, demonstrating better improvements and scalability under various maneuvers.
