用局部贝叶斯优化自动调参，避免控制器崩溃
Local Bayesian Optimization for Controller Tuning with Crash Constraints
- 基于局部贝叶斯优化，在未知可行域内安全搜索
- 实验证明可显著减少调参时间与资源消耗
- 适合需要高安全性、自动化调参的控制场景
控制器调参对闭环性能至关重要，但通常依赖人工调整。尽管贝叶斯优化（BO）被证明是高效的数据驱动自动调参方法，但在大规模高维搜索空间中仍具挑战性。本文将近期提出的局部贝叶斯优化方法扩展至包含崩溃约束的情形，即控制器仅能在事先未知的可行区域内成功评估。通过仿真和硬件实验验证了所提方法的高效性。结果表明，局部贝叶斯优化能有效提升控制器性能，并大幅减少调参所需的时间与资源。
原文摘要 · Abstract (English)
Controller tuning is crucial for closed-loop performance but often involves manual adjustments. Although Bayesian optimization (BO) has been established as a data-efficient method for automated tuning, applying it to large and high-dimensional search spaces remains challenging. We extend a recently proposed local variant of BO to include crash constraints, where the controller can only be successfully evaluated in an a-priori unknown feasible region. We demonstrate the efficiency of the proposed method through simulations and hardware experiments. Our findings showcase the potential of local BO to enhance controller performance and reduce the time and resources necessary for tuning.
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