基于人工智能的模型估计精确定位阶段,采用多重控制切换
Fu-Cheng Wang1, Bo-Xuan Zhong1, Chi-Wei Wen1
1Department of Mechanical Engineering, National Taiwan University, Taipei 106319, Taiwan.
Micromachines
|December 31, 2025
概括
本研究引入了一个人工智能框架,用于实时压电变频器 (PZT) 模型估计. 它通过通过 eXtreme Gradient Boosting (XGBoost) 持续更新系统模型来提高控制性能,提高准确性和适应性.
科学领域:
- 控制系统工程 控制系统工程
- 人工智能的人工智能
- 材料科学 材料科学 材料科学
背景情况:
- 对于压电传感器 (PZT) 阶段的传统固定控制器,响应速度和流性之间存在权衡.
- 现有的多控制器切换机制依赖于名义工厂模型,忽视操作变化并限制性能.
- 准确的实时系统建模对于动态环境中的自适应控制至关重要.
研究的目的:
- 开发一个实时模型估计框架,以增强压电传感器 (PZT) 阶段控制.
- 通过结合自适应模型更新来提高多控制器切换机制的性能.
- 解决传统控制器和忽视系统变化的现有切换机制的局限性.
主要方法:
- 实现基于人工智能的框架,用于实时模型估计.
- 使用 eXtreme渐变增强 (XGBoost) 算法来持续更新系统模型.
- 实时模型估计器与适应性控制的多控制器切换机制的集成.
主要成果:
- 拟议的基于XGBoost的模型估计器通过不断更新系统模型,显著提高了预测准确性.
- 基于更新模型的控制器的自适应调整可以提高开关机制的整体性能.
- 模拟和实验验证实实时估计和自适应控制方法的有效性.
结论:
- 开发的实时模型估计框架有效地提高了压电传感器 (PZT) 阶段控制性能.
- 基于XGBoost的自适应建模为具有操作变化的系统提供了强大的解决方案.
- 实时估计和自适应控制的整合为先进的控制系统提供了一个有希望的方向.
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