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

TL;DR

This paper presents a Bayesian optimization framework for tuning nonlinear path-following controllers on real robots, significantly reducing the number of trials needed to achieve optimal performance.

Contribution

The paper introduces a model-free Bayesian optimization approach for tuning complex nonlinear controllers on real hardware, addressing evaluation constraints.

Findings

BO improves controller performance within 32 trials

Efficiently locates high-performing parameter regions

Applicable to real-world robotic systems

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.