用于非线性动力系统高效学习的模型融合
Model fusion for efficient learning of nonlinear dynamical systems
AI总结：
本文提出一种融合多工作点线性模型与稀疏优化的方法，以高效构建简约非线性动态模型，克服单一线性模型和传统多模型方法在宽工况工业过程建模中的不足。
摘要：
在基于模型的工业过程控制背景下，围绕指定工作点开发数据驱动的线性动力学模型是一种常见做法。然而，在涉及更宽工作条件的应用中，使用单一线性动态模型来表示动力学往往不够充分，需要非线性模型或多个线性模型来适应非线性行为。前者的开发面临需要覆盖多个水平的广泛实验、在标称产品质量上做出显著妥协以及在更宽工作条件下处理未测量扰动等困难；后者则面临模型切换调度以及对中间工作区域动力学描述不足的挑战。为克服这些挑战，我们提出一种高效方法，通过在多个工作点从数据开发多个线性模型，对单个模型仿真所获得的数据特征进行提升，以充分适应潜在的非线性行为，最后利用稀疏优化技术获得简约的非线性动态模型。通过仿真案例研究验证了所提算法的性能和有效性。
英文摘要：
In the context of model-based control of industrial processes, it is a common practice to develop a data-driven linear dynamical model around a specified operating point. However, in applications involving wider operating conditions, representation of the dynamics using a single linear dynamic model is often inadequate, requiring either a nonlinear model or multiple linear models to accommodate the nonlinear behaviour. While the development of the former suffers from the requirements of extensive experiments spanning multiple levels, significant compromise in the nominal product quality and dealing with unmeasured disturbances over wider operating conditions, the latter faces the challenge of model switch scheduling and inadequate description of dynamics for the operating regions in-between. To overcome these challenges, we propose an efficient approach to obtain a parsimonious nonlinear dynamic model by developing multiple linear models from data at multiple operating points, lifting the data features obtained from individual model simulations to adequately accommodate the underlying nonlinear behaviour and finally, sparse optimization techniques to obtain a parsimonious model. The performance and effectiveness of the proposed algorithm is demonstrated through simulation case studies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
