Bayesian Optimization Parameter Tuning Framework for a Lyapunov Based Path Following Controller
Abstract
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.
Cite
@article{arxiv.2512.12649,
title = {Bayesian Optimization Parameter Tuning Framework for a Lyapunov Based Path Following Controller},
author = {Zhewen Zheng and Wenjing Cao and Hongkang Yu and Mo Chen and Takashi Suzuki},
journal= {arXiv preprint arXiv:2512.12649},
year = {2026}
}
Comments
The authors request withdrawal because the current arXiv version does not reflect the complete and finalized authorship record of the manuscript. The author list and contribution record require correction before further public dissemination