一个基于干扰观察器和神经网络的有限时间滑动模式控制器,用于歇斯底里系统,用于压电执行器
Liqun Cheng1,2, Wanzhong Chen1, Liguo Tian2
1College of Communication Engineering, Jilin University, Changchun 130012, China.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
本研究介绍了一种新的有限时间滑动模式控制器 (SMC),使用辐射基函数 (RBF) 神经网络 (NN) 和干扰观察器 (DOB) 来提高压电执行器 (PEA) 控制精度. 该方法有效地减轻了hysteresis,并提高了高精度定位系统的跟踪性能.
科学领域:
- 机械电子和控制系统
- 材料科学与工程 材料科学与工程
- 机器人和自动化 机器人和自动化
背景情况:
- 压电执行器 (PEAs) 对于微/纳米高精度定位至关重要,因为它们的高分辨率和频率响应.
- PEAs表现出非线性歇斯底里,受输入频率的影响,这使精确的控制变得复杂.
- 现有的PEA适应性控制研究解决了跟踪控制的挑战.
研究的目的:
- 为压电执行器 (PEAs) 开发一种先进的控制策略,克服固有的歇斯底里非线性.
- 为了提高PEAs在高精度应用中的跟踪控制精度和稳定性.
- 提出一种新的控制方法,消除了对显式歇斯底里模型的需求.
主要方法:
- 实现一个有限时间滑动模式控制器 (SMC),集成到一个辐射基函数 (RBF) 神经网络 (NN).
- 使用一种新的有限时间适应性干扰观察器 (DOB) 来估计没有先前知识的系统干扰.
- 使用 RBF-NN 来动态补偿歇斯底里,取代传统的歇斯底里模型.
主要成果:
- 拟议的基于RBF-NN的DOB控制器证明了闭环系统的实际有限时间稳定性.
- 在各种频率上,追踪错误在有限的时间内汇聚到零的小邻里.
- 在商业PEA平台上的实验验证证证了该方法在提高追踪精度方面的可行性和有效性.
结论:
- 开发的具有RBF-NN和自适应DOB的有限时间SMC为精确的PEA控制提供了强大的解决方案.
- 这种方法显著提高了跟踪控制的准确性,特别是在动态条件和不同频率下.
- 该方法与显式歇斯底里模型的独立性及其简单的DOB结构有助于其实际应用.
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