用少次实测高效调优车辆控制器参数
Efficient Learning of Vehicle Controller Parameters via Multi-Fidelity Bayesian Optimization: From Simulation to Experiment
- 融合高低保真度数据的贝叶斯优化方法
- 仅需少量实测即达高精度控制性能
- 适合工业界智能汽车参数调优场景
车辆控制器参数调优在汽车研发中仍面临成本高、耗时长的问题。传统方法依赖大量实地测试，效率低下。本文提出一种多保真度贝叶斯优化方法，利用低保真度仿真数据与极少量真实实验数据，高效学习最优控制器参数。该方法在保持工业界常用两阶段开发流程的基础上，显著减少人工调参与昂贵的路测需求。核心贡献是将自回归多保真度高斯过程模型集成到贝叶斯优化中，实现不同保真度间知识迁移，且无需在真实测试中额外进行低保真度评估。通过仿真与真实实验验证，本方法仅需极少实测即可达到高质量控制性能，展现出在工业级智能车辆控制调优中的实用与可扩展潜力。
原文摘要 · Abstract (English)
Parameter tuning for vehicle controllers remains a costly and time-intensive challenge in automotive development. Traditional approaches rely on extensive real-world testing, making the process inefficient. We propose a multi-fidelity Bayesian optimization approach that efficiently learns optimal controller parameters by leveraging both low-fidelity simulation data and a very limited number of real-world experiments. Our approach significantly reduces the need for manual tuning and expensive field testing while maintaining the standard two-stage development workflow used in industry. The core contribution is the integration of an auto-regressive multi-fidelity Gaussian process model into Bayesian optimization, enabling knowledge transfer between different fidelity levels without requiring additional low-fidelity evaluations during real-world testing. We validate our approach through both simulation studies and realworld experiments. The results demonstrate that our method achieves high-quality controller performance with only very few real-world experiments, highlighting its potential as a practical and scalable solution for intelligent vehicle control tuning in industrial applications.
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