Systematic Evaluation of Initial States and Exploration-Exploitation Strategies in PID Auto-Tuning: A Framework-Driven Approach Applied on Mobile Robots
🤖 AI Summary
This work addresses the impact of initial system state on convergence and the exploration–exploitation trade-off in automatic PID controller tuning. It presents the first empirical study conducted on real mobile robots—both omnidirectional and differential-drive platforms. We propose a synergistic parameter-tuning framework integrating Bayesian optimization with differential evolution, systematically quantifying how initial state and exploration intensity affect convergence rate, overshoot, and dynamic performance. Results demonstrate that the initial system state significantly influences tuning efficiency and closed-loop stability; moreover, exploration intensity exhibits strong coupling with the selection of initial sampling points—joint optimization of both factors enhances robustness and convergence reliability. The study establishes a reproducible evaluation paradigm, providing critical empirical evidence and methodological support for adaptive PID tuning in industrial applications. (149 words)
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Evaluates impact of initial states on PID auto-tuning convergence
Assesses exploration-exploitation balance in Bayesian Optimization and Differential Evolution
Tests framework on mobile robots for realistic PID tuning insights
Innovation
Methods, ideas, or system contributions that make the work stand out.
Framework evaluates initial states impact on PID tuning
Uses Bayesian Optimization and Differential Evolution
Tested on omnidirectional and differential drive robots
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