Systematic Evaluation of Initial States and Exploration-Exploitation Strategies in PID Auto-Tuning: A Framework-Driven Approach Applied on Mobile Robots

TL;DR

This paper introduces a framework to systematically evaluate how initial states and exploration-exploitation strategies affect PID auto-tuning on mobile robots, providing empirical insights into convergence and control performance.

Contribution

It presents a novel framework for assessing the impact of initial conditions and strategies on PID auto-tuning using Bayesian Optimization and Differential Evolution on real robotic systems.

Findings

Initial states significantly influence convergence speed.

Exploration-exploitation balance affects control accuracy.

Framework validated on two different mobile robots.

Abstract

PID controllers are widely used in control systems because of their simplicity and effectiveness. Although advanced optimization techniques such as Bayesian Optimization and Differential Evolution have been applied to address the challenges of automatic tuning of PID controllers, the influence of initial system states on convergence and the balance between exploration and exploitation remains underexplored. Moreover, experimenting the influence directly on real cyber-physical systems such as mobile robots is crucial for deriving realistic insights. In the present paper, a novel framework is introduced to evaluate the impact of systematically varying these factors on the PID auto-tuning processes that utilize Bayesian Optimization and Differential Evolution. Testing was conducted on two distinct PID-controlled robotic platforms, an omnidirectional robot and a differential drive mobile robot, to assess the effects on convergence rate, settling time, rise time, and overshoot percentage. As a result, the experimental outcomes yield evidence on the effects of the systematic variations, thereby providing an empirical basis for future research studies in the field.