.png){kind=icon}
返回
A Data-Driven Bayesian Koopman Learning Method for Modeling Hysteresis Dynamics
.png){kind=icon}
PRE
AI
DOI：10.1109/TNNLS.2023.3288752.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Exploring the mechanism of hysteresis dynamics may facilitate the analysis and controller design to alleviate detrimental effects. Conventional models, such as the Bouc-Wen and Preisach models consist of complicated nonlinear structures, limiting the applications of hysteresis systems for high-speed and high-precision positioning, detection, execution, and other operations. In this article, a Bayesian Koopman (B-Koopman) learning algorithm is therefore developed to characterize hysteresis dynamics. Essentially, the proposed scheme establishes a simplified linear representation with time delay for hysteresis dynamics, where the properties of the original nonlinear system are preserved. Furthermore, model parameters are optimized via sparse Bayesian learning together with an iterative strategy, which simplifies the identification procedure and reduces modeling errors. Extensive experimental results on piezoelectric positioning are elaborated to substantiate the effectiveness and superiority of the proposed B-Koopman algorithm for learning hysteresis dynamics.
Keyword:
Index Terms- Hysteresis
Koopman operator
modeling
piezo-electric actuators (PEAs)
期刊
IF:
8.9
论文数:
7.5K
被引数:
7.2W
机构
引用论文
分享收藏
分享收藏Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control非线性动力系统的线性预测器: Koopman算子满足模型预测控制.png)
AUTOMATICA
OAAI
AUTOMATICAIF5.9
OAAI分享收藏A Dynamic Model of Stick-Slip Piezoelectric Actuators Considering the Deformation of Overall System
PREAI
分享收藏Deep Koopman model predictive control for enhancing transient stability in power grids提高电网暂态稳定性的深度Koopman模型预测控制
PREAI
分享收藏
查看更多内容