Parameter tuning in real-world experiments is constrained by the limited evaluation budget available on hardware. The path-following controller studied in this paper reflects a typical situation in nonlinear geometric controller, where multiple gains influence the dynamics through coupled nonlinear terms. Such interdependence makes manual tuning inefficient and unlikely to yield satisfactory performance within a practical number of trials. To address this challenge, we propose a Bayesian optimization (BO) framework that treats the closed-loop system as a black box and selects controller gains using a Gaussian-process surrogate. BO offers model-free exploration, quantified uncertainty, and data-efficient search, making it well suited for tuning tasks where each evaluation is costly. The framework is implemented on Honda's AI-Formula three-wheeled robot and assessed through repeated full-lap experiments on a fixed test track. The results show that BO improves controller performance within 32 trials, including 15 warm-start initial evaluations, indicating that it can efficiently locate high-performing regions of the parameter space under real-world conditions. These findings demonstrate that BO provides a practical, reliable, and data-efficient tuning approach for nonlinear path-following controllers on real robotic platforms.

翻译：在实际实验中进行参数调优时，硬件上可用的评估预算有限。本文研究的路径跟随控制器反映了非线性几何控制器中的典型情况：多个增益通过耦合非线性项影响系统动力学。这种相互依赖性使得手动调优效率低下，且难以在实用次数的试验中获得令人满意的性能。为解决这一挑战，我们提出了一种贝叶斯优化（BO）框架，将闭环系统视为黑箱，并使用高斯过程代理模型选择控制器增益。BO提供无模型探索、量化不确定性和数据高效搜索，使其非常适合每次评估代价高昂的调优任务。该框架已在本田AI-Formula三轮机器人上实现，并通过在固定测试赛道上进行重复全圈实验进行评估。结果表明，BO能在32次试验（包括15次热启动初始评估）内改善控制器性能，说明它能在真实条件下高效定位参数空间的高性能区域。这些发现证明，BO为真实机器人平台上的非线性路径跟随控制器提供了一种实用、可靠且数据高效的调优方法。