# Improving Computational Cost of Bayesian Optimization for Controller Tuning with a Multi-stage Tuning Framework

arXiv: https://arxiv.org/abs/2411.05355 · HTML: https://mlsanity.com/paper/2411.05355/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Nov 2024.

## Built on by (most cited first, 1 of 1)

- [Systematic Evaluation of Initial States and Exploration-Exploitation Strategies in PID Auto-Tuning: A Framework-Driven Approach Applied on Mobile Robots](https://mlsanity.com/paper/2505.03159/) (2025)

## Abstract (arXiv)

Control auto-tuning for industrial and robotic systems, when framed as an optimization problem, provides an excellent means to tune these systems. However, most optimization methods are computationally costly, and this is problematic for high-dimension control parameter spaces. In this paper, we present a multi-stage control tuning framework that decomposes control tuning into subtasks, each with a reduced-dimension search space. We show formally that this framework reduces the sample complexity of the control-tuning task. We empirically validate this result by applying a Bayesian optimization approach to tuning multiple PID controllers in an unmanned underwater vehicle benchmark system. We demonstrate an 86% decrease in computational time and 36% decrease in sample complexity.