基于内部代梯度估计器的非线性三明治系统的参数识别,受量化传感器和摩擦非线性影响
Huijie Lei1, Yanwei Zhang2, Xikun Lu1
1School of Electronic, Electrical and Unmanned Aerial Vehicle, Anyang University of Technology, Anyang, People's Republic of China.
PloS one
|April 29, 2025
概括
本研究引入了一种改进的梯度估计方法,用于传感器量化和摩擦的非线性系统. 新方法提高了对复杂系统的参数估计准确度和融合速度.
科学领域:
- 控制系统工程 控制系统工程
- 非线性系统识别 非线性系统识别
- 信号处理 信号处理
背景情况:
- 现有的多创新梯度方法 (MISG) 在非线性系统的参数估计方面存在局限性.
- 量子化传感器和摩擦非线性在准确的系统建模中带来了重大挑战.
- 对于复杂的动态系统,需要强大而高效的参数估计技术.
研究的目的:
- 为非线性三明治系统提出一个内部代标量创新梯度估计方法.
- 解决传统MISG的缺点,特别是关于冗余参数估计和多创新长度.
- 在具有传感器定量化和摩擦的系统中提高参数估计的准确性和趋同率.
主要方法:
- 分解方法来导出识别模型,避免冗余的参数估计.
- 适应性波器利用先前的系统知识进行最佳数据选择.
- 内部代原理将多创新更新转换为标量创新更新.
- 对于低于最佳的初始估计的触发机制,以加速参数适应性规律.
主要成果:
- 通过模型分解成功避免了冗余参数估计.
- 通过将多项创新转换为标量创新更新,实现了积极的估计性能.
- 对参数适应性定律的快速收率证明.
- 通过数值模拟和在机电系统上的实验测试来验证拟议的方法.
结论:
- 拟议的内部代标量创新梯度估计方法有效地识别非线性三明治系统中的参数.
- 该方法克服了传统MISG的局限性,提供了更高的准确性和更快的融合.
- 该策略强大,适用于具有传感器定量化和摩擦非线性现实世界系统.
相关概念视频
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