用数字孪生和真实数据联合优化控制器，提升调参效率。
Guided Multi-Fidelity Bayesian Optimization for Data-driven Controller Tuning with Digital Twins
- 融合数字孪生与实测数据，通过学习修正模型补偿仿真误差。
- 自适应调整采样策略，显著减少调参所需实验次数。
- 适合硬件受限、仿真精度不高的闭环控制系统调优。
我们提出一种引导式多保真度贝叶斯优化框架，用于数据高效的控制器调参，整合经校正的数字孪生仿真与真实测量数据。该方法针对保真度有限或成本低廉的近似仿真场景。为解决模型失配问题，构建了一个带学习修正模型的多保真度代理模型，利用真实数据优化数字孪生预测。采用自适应的成本感知采集函数，在期望改进、保真度和采样成本间取得平衡。随着新测量数据的到来，系统可动态重估数字孪生精度，自适应调整跨源相关性及采集函数。这确保高精度仿真被更频繁使用，低精度仿真数据则被适当降权。在机器人驱动硬件上的实验及数值研究均表明，相比标准贝叶斯优化和多保真度方法，本方法显著提升了调参效率。
原文摘要 · Abstract (English)
We propose a \textit{guided multi-fidelity Bayesian optimization} framework for data-efficient controller tuning that integrates corrected digital twin simulations with real-world measurements. The method targets closed-loop systems with limited-fidelity simulations or inexpensive approximations. To address model mismatch, we build a multi-fidelity surrogate with a learned correction model that refines digital twin estimates using real data. An adaptive cost-aware acquisition function balances expected improvement, fidelity, and sampling cost. Our method ensures adaptability as new measurements arrive. The digital twin accuracy is re-estimated, dynamically adapting both cross-source correlations and the acquisition function. This ensures that accurate simulations are used more frequently, while inaccurate simulation data are appropriately downweighted. Experiments on robotic drive hardware and supporting numerical studies demonstrate that our method enhances tuning efficiency compared to standard Bayesian optimization and multi-fidelity methods.
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