Nonlinear System Identification with Application to Combustion Instability Control

加州大学圣迭戈分校机械与航天工程系罗伯特·R·比特米德，加州大学圣迭戈分校非线性系统辨识及其在燃烧不稳定控制中的应用。系统辨识是一门利用实验测量数据和物理推理的候选模型结构来拟合数学动态模型的科学。在开发复杂和不精确的已知系统的模型时，在分析它们之前，识别是至关重要的。该项目致力于开发用于非线性系统辨识的技术和基础理论。目前，对于如何处理这一案件，缺乏工具和指导。虽然重点是科学和广泛的，这项研究将继续与理论一起考虑燃烧不稳定性控制的主要驱动应用，对喷气发动机和燃气动力涡轮感兴趣。该项目有几个阶段，在这些阶段中，一般概念得到发展，然后在燃烧不稳定性的背景下进行探索。早期的目标将是开发合适的适配度指标，然后再测试模型的可信度。这些方法应该捕捉到来自非线性振荡系统的数据记录中的重要特征，而忽略不重要的细节。在这里，可以用已经与数据拟合的模型进行测试。具体地说，分叉分析等非线性工具可以用于评估模型的性质，这些性质从一个实验保存到另一个实验，并可能构成控制设计的基础。理论任务之一是解决振荡器等系统的模型验证概念。也就是说，如何开发一项能够揭示使用某种标准拟合的特定候选模型的弱点的实验。这与对已确定的模型的信心以及对其是否适合使用的不确定性密切相关。如果开发出通用的概念和有意义的测试，这些工具的应用就有现成的市场。事实上，最近非线性控制设计在理论和实践上都取得了很大进展，但它需要已知可靠性的好模型才能走得更远。

Abstract-0200449Robert R. Bitmead, U. of Cal-San DiegoNonlinear System Identification with Application to Combustion Instabillty ControlRobert R Bitmead, Department of Mechanical & Aerospace Engineering, U of California-San Diego.System Identification is the science of fitting mathematical dynamical models using measured data from experiments together with candidate model structures from physical reasoning. Identification is critical in developing models of complex and imprecisely known systems prior to their analysis. This project is concerned with developing techniques and underpinning theories for nonlinear system identification. At present there is a dearth of tools and guidance of how to proceed in this case. While the focus is scientific and broad, the study will proceed by considering in tandem with theory a major driving application of combustion instability control, of interest in jet engines and gas power turbines.The project has phases in which general concepts are developed and then are explored in the context of the combustion instabilities. Developing suitable measures of fit and then tests of confidence of models will be early goals. These should capture the important features in the data records from the nonlinear oscillating system while ignoring the unimportant detail. Here tests can be made with models already fitted to the data. Specifically nonlinear tools such as bifurcation analysis then can be applied for the assessment of model properties which are preserved from experiment to experiment and which might form the basis of control design.Chief among the theoretical tasks is to address the concept of model validation for such systems as oscillators. That is, how might one develop an experiment, which is capable of revealing the weaknesses of a particular candidate model fitted using some criterion. This is intimately connected with confidence in the identified model and with uncertainty in its suitability for a use. Should generalized concepts and meaningful tests be developed, there is a ready market for the application of these tools. Indeed, nonlinear control design has moved forward greatly recently in theory and practice, but it needs good models of known reliability to go much further.