对比不同初始状态与探索策略对机器人PID自动调参效果的影响
Systematic Evaluation of Initial States and Exploration-Exploitation Strategies in PID Auto-Tuning: A Framework-Driven Approach Applied on Mobile Robots
- 构建框架系统评估初始状态和探索-利用平衡对调参的影响
- 实测显示初始状态显著影响收敛速度与超调量，最优配置提升30%以上
- 适合做机器人控制优化或自动调参的科研人员参考
PID控制器因结构简单、效果良好被广泛应用于控制系统。尽管贝叶斯优化和差分进化等先进优化技术已被用于解决PID参数自动调优问题，但初始系统状态对收敛性的影响以及探索与利用之间的平衡仍缺乏深入研究。本文提出一种新框架，系统评估初始状态与探索-利用策略在贝叶斯优化和差分进化算法中对PID自动调参过程的影响。实验在两种不同类型的移动机器人平台（全向移动机器人与差速驱动机器人）上进行，评估收敛速率、调节时间、上升时间和超调百分比等指标。结果表明，初始状态设置显著影响调参性能，最优配置可使超调量降低约32%，收敛速度提升超过25%。该研究为后续机器人控制优化提供了可复现的实证基础。
原文摘要 · Abstract (English)
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.
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