使用前神经网络和反集成快速终端滑动模式控制的压电执行器的组合控制
Eneko Artetxe1, Oscar Barambones1, Isidro Calvo1
1System Engineering and Automation Department, Faculty of Engineering of Vitoria-Gasteiz, Basque Country University (UPV/EHU), 01006 Vitoria-Gasteiz, Spain.
本研究引入了基于人工神经网络的集成快速终端滑动模式控制 (基于ANN的IFTSMC),以提高压电执行器 (PEA) 的精度. 新型控制器有效地模拟和补偿歇斯底里,提高工业应用中的定位精度.
科学领域:
- 控制系统工程 控制系统工程
- 材料科学 材料科学 材料科学
- 机器人技术 机器人技术 机器人技术
背景情况:
- 压电执行器 (PEAs) 对于纳米技术和半导体制造中的微定位至关重要.
- PEA表现出复杂的hysteresis动态,挑战精确的建模和控制.
- 现有的控制方法很难完全解决PEA非线性问题.
研究的目的:
- 开发一种新的控制策略,以提高PEA定位系统的精度和可靠性.
- 为了应对在PEAs中建模复杂的hysteresis动态的挑战.
- 为了提高PEA控制中的跟踪精度和干扰排斥能力.
主要方法:
- 实施人工神经网络 (ANN) 用于歇斯底里模型.
- 一个完整的快速终端滑动模式控制 (IFTSMC) 策略的设计.
- 实时实验验证和与PID控制器进行比较.
主要成果:
- 拟议的基于ANN的IFTSMC控制器表现出稳定性和有效补偿PEA歇斯底里.
- 实验结果显示,与传统的PID控制器相比,定位误差显著减少.
- 新的控制结构提高了跟踪精度和干扰排斥.
结论:
- 基于ANN的IFTSMC控制器提供了一个强大的解决方案,用于精确控制PEAs.
- 这种方法提高了微定位系统的可靠性和性能.
- 该研究验证了复杂的执行器动态的先进控制策略的有效性.
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