Systematic Evaluation of Initial States and Exploration-Exploitation Strategies in PID Auto-Tuning: A Framework-Driven Approach Applied on Mobile Robots
This work addresses the underexplored influence of initial conditions and strategy balance in PID auto-tuning for mobile robots, providing an empirical basis for future research, but it is incremental as it builds on existing optimization techniques.
The paper tackled the problem of how initial states and exploration-exploitation strategies affect PID auto-tuning convergence and performance, using Bayesian Optimization and Differential Evolution on mobile robots, with results showing empirical evidence on convergence rate, settling time, rise time, and overshoot percentage.
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.