论文标题
以LPV形式表示的一类非线性模型的参数可识别性方法
An approach to parameter identifiability for a class of nonlinear models represented in LPV form
论文作者
论文摘要
在几个基于模型的系统维护问题中，参数用于表示组件，设备降解等的未知特征。这允许对恒定，慢速变化的术语进行建模。这些参数的可识别性是估计它们的重要条件。线性参数变化（LPV）模型正在越来越多地用作线性模型和非线性模型之间的桥梁。存在可以以LPV形式重写某些非线性模型的技术。但是，这些模型的可识别性问题仍处于新生的阶段。在本文中，我们提出了一种验证LPV状态空间模型未知参数的可识别性的方法。它利用奇偶校验空间之类的公式来消除模型的状态。分析所得的输入输出参数方程，以验证原始模型的可识别性或未知参数的子集。这种方法为连续时间和离散时间模型提供了一个框架，我们使用示例对其进行了说明。
In several model-based system maintenance problems, parameters are used to represent unknown characteristics of a component, equipment degradation, etc. This allows for modelling constant, slow-varying terms. The identifiability of these parameters is an important condition to estimate them. Linear Parameter Varying (LPV) models are being increasingly used in the industries as a bridge between linear and nonlinear models. Techniques exist that can rewrite some nonlinear models in LPV form. However, the problem of identifiability of these models is still at a nascent stage. In this paper, we propose an approach to verify identifiability of unknown parameters for LPV state-space models. It makes use of a parity-space like formulation to eliminate the states of the model. The resulting input-output-parameter equation is analysed to verify the identifiability of the original model or a subset of unknown parameters. This approach provides a framework for both continuous-time and discrete-time models and we illustrate it using examples.
