Bayesian Optimization Parameter Tuning Framework for a Lyapunov Based Path Following Controller

• URL: http://arxiv.org/abs/2512.12649v1
• Date: Sun, 14 Dec 2025 11:35:53 GMT
• Title: Bayesian Optimization Parameter Tuning Framework for a Lyapunov Based Path Following Controller
• Abstract summary: tuning in real-world experiments is constrained by the limited evaluation budget available on hardware.<n>The framework is implemented on Honda's AI-Formula three-wheeled robot.<n>Results show that BO improves controller performance within 32 trials, including 15 warm-start initial evaluations.
• Score: 7.212376559978978
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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